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Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 2024:S0140-6736(24)00757-8. [PMID: 38642570 DOI: 10.1016/s0140-6736(24)00757-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 03/07/2024] [Accepted: 04/12/2024] [Indexed: 04/22/2024]
Abstract
BACKGROUND Detailed, comprehensive, and timely reporting on population health by underlying causes of disability and premature death is crucial to understanding and responding to complex patterns of disease and injury burden over time and across age groups, sexes, and locations. The availability of disease burden estimates can promote evidence-based interventions that enable public health researchers, policy makers, and other professionals to implement strategies that can mitigate diseases. It can also facilitate more rigorous monitoring of progress towards national and international health targets, such as the Sustainable Development Goals. For three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has filled that need. A global network of collaborators contributed to the production of GBD 2021 by providing, reviewing, and analysing all available data. GBD estimates are updated routinely with additional data and refined analytical methods. GBD 2021 presents, for the first time, estimates of health loss due to the COVID-19 pandemic. METHODS The GBD 2021 disease and injury burden analysis estimated years lived with disability (YLDs), years of life lost (YLLs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries using 100 983 data sources. Data were extracted from vital registration systems, verbal autopsies, censuses, household surveys, disease-specific registries, health service contact data, and other sources. YLDs were calculated by multiplying cause-age-sex-location-year-specific prevalence of sequelae by their respective disability weights, for each disease and injury. YLLs were calculated by multiplying cause-age-sex-location-year-specific deaths by the standard life expectancy at the age that death occurred. DALYs were calculated by summing YLDs and YLLs. HALE estimates were produced using YLDs per capita and age-specific mortality rates by location, age, sex, year, and cause. 95% uncertainty intervals (UIs) were generated for all final estimates as the 2·5th and 97·5th percentiles values of 500 draws. Uncertainty was propagated at each step of the estimation process. Counts and age-standardised rates were calculated globally, for seven super-regions, 21 regions, 204 countries and territories (including 21 countries with subnational locations), and 811 subnational locations, from 1990 to 2021. Here we report data for 2010 to 2021 to highlight trends in disease burden over the past decade and through the first 2 years of the COVID-19 pandemic. FINDINGS Global DALYs increased from 2·63 billion (95% UI 2·44-2·85) in 2010 to 2·88 billion (2·64-3·15) in 2021 for all causes combined. Much of this increase in the number of DALYs was due to population growth and ageing, as indicated by a decrease in global age-standardised all-cause DALY rates of 14·2% (95% UI 10·7-17·3) between 2010 and 2019. Notably, however, this decrease in rates reversed during the first 2 years of the COVID-19 pandemic, with increases in global age-standardised all-cause DALY rates since 2019 of 4·1% (1·8-6·3) in 2020 and 7·2% (4·7-10·0) in 2021. In 2021, COVID-19 was the leading cause of DALYs globally (212·0 million [198·0-234·5] DALYs), followed by ischaemic heart disease (188·3 million [176·7-198·3]), neonatal disorders (186·3 million [162·3-214·9]), and stroke (160·4 million [148·0-171·7]). However, notable health gains were seen among other leading communicable, maternal, neonatal, and nutritional (CMNN) diseases. Globally between 2010 and 2021, the age-standardised DALY rates for HIV/AIDS decreased by 47·8% (43·3-51·7) and for diarrhoeal diseases decreased by 47·0% (39·9-52·9). Non-communicable diseases contributed 1·73 billion (95% UI 1·54-1·94) DALYs in 2021, with a decrease in age-standardised DALY rates since 2010 of 6·4% (95% UI 3·5-9·5). Between 2010 and 2021, among the 25 leading Level 3 causes, age-standardised DALY rates increased most substantially for anxiety disorders (16·7% [14·0-19·8]), depressive disorders (16·4% [11·9-21·3]), and diabetes (14·0% [10·0-17·4]). Age-standardised DALY rates due to injuries decreased globally by 24·0% (20·7-27·2) between 2010 and 2021, although improvements were not uniform across locations, ages, and sexes. Globally, HALE at birth improved slightly, from 61·3 years (58·6-63·6) in 2010 to 62·2 years (59·4-64·7) in 2021. However, despite this overall increase, HALE decreased by 2·2% (1·6-2·9) between 2019 and 2021. INTERPRETATION Putting the COVID-19 pandemic in the context of a mutually exclusive and collectively exhaustive list of causes of health loss is crucial to understanding its impact and ensuring that health funding and policy address needs at both local and global levels through cost-effective and evidence-based interventions. A global epidemiological transition remains underway. Our findings suggest that prioritising non-communicable disease prevention and treatment policies, as well as strengthening health systems, continues to be crucially important. The progress on reducing the burden of CMNN diseases must not stall; although global trends are improving, the burden of CMNN diseases remains unacceptably high. Evidence-based interventions will help save the lives of young children and mothers and improve the overall health and economic conditions of societies across the world. Governments and multilateral organisations should prioritise pandemic preparedness planning alongside efforts to reduce the burden of diseases and injuries that will strain resources in the coming decades. FUNDING Bill & Melinda Gates Foundation.
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Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 2024:S0140-6736(24)00367-2. [PMID: 38582094 DOI: 10.1016/s0140-6736(24)00367-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 02/15/2024] [Accepted: 02/22/2024] [Indexed: 04/08/2024]
Abstract
BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation.
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Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950-2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021. Lancet 2024:S0140-6736(24)00476-8. [PMID: 38484753 DOI: 10.1016/s0140-6736(24)00476-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 12/08/2023] [Accepted: 03/06/2024] [Indexed: 04/13/2024]
Abstract
BACKGROUND Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020-21 COVID-19 pandemic period. METHODS 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. FINDINGS Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5-65·1] decline), and increased during the COVID-19 pandemic period (2020-21; 5·1% [0·9-9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98-5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50-6·01) in 2019. An estimated 131 million (126-137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7-17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8-24·8), from 49·0 years (46·7-51·3) to 71·7 years (70·9-72·5). Global life expectancy at birth declined by 1·6 years (1·0-2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67-8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4-52·7]) and south Asia (26·3% [9·0-44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. INTERPRETATION Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic. FUNDING Bill & Melinda Gates Foundation.
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4
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Nguyen M, Dzianach PA, Castle PECW, Rumisha SF, Rozier JA, Harris JR, Gibson HS, Twohig KA, Vargas-Ruiz CA, Bisanzio D, Cameron E, Weiss DJ, Bhatt S, Gething PW, Battle KE. Trends in treatment-seeking for fever in children under five years old in 151 countries from 1990 to 2020. PLOS Glob Public Health 2023; 3:e0002134. [PMID: 37611001 PMCID: PMC10446233 DOI: 10.1371/journal.pgph.0002134] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 07/11/2023] [Indexed: 08/25/2023]
Abstract
Access to medical treatment for fever is essential to prevent morbidity and mortality in individuals and to prevent transmission of communicable febrile illness in communities. Quantification of the rates at which treatment is accessed is critical for health system planning and a prerequisite for disease burden estimates. In this study, national data on the proportion of children under five years old with fever who were taken for medical treatment were collected from all available countries in Africa, Latin America, and Asia (n = 91). We used generalised additive mixed models to estimate 30-year trends in the treatment-seeking rates across the majority of countries in these regions (n = 151). Our results show that the proportions of febrile children brought for medical treatment increased steadily over the last 30 years, with the greatest increases occurring in areas where rates had originally been lowest, which includes Latin America and Caribbean, North Africa and the Middle East (51 and 50% increase, respectively), and Sub-Saharan Africa (23% increase). Overall, the aggregated and population-weighted estimate of children with fever taken for treatment at any type of facility rose from 61% (59-64 95% CI) in 1990 to 71% (69-72 95% CI) in 2020. The overall population-weighted average for fraction of treatment in the public sector was largely unchanged during the study period: 49% (42-58 95% CI) sought care at public facilities in 1990 and 47% (44-52 95% CI) in 2020. Overall, the findings indicate that improvements in access to care have been made where they were most needed, but that despite rapid initial gains, progress can plateau without substantial investment. In 2020 there remained significant gaps in care utilisation that must be factored in when developing control strategies and deriving disease burden estimates.
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Affiliation(s)
- Michele Nguyen
- Asian School of the Environment, Nanyang Technological University, Singapore, Singapore
| | | | | | - Susan F. Rumisha
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA, Australia
| | - Jennifer A. Rozier
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA, Australia
| | - Joseph R. Harris
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA, Australia
| | | | - Katherine A. Twohig
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | | | - Donal Bisanzio
- RTI International, Washington, District of Columbia, United States of America
- Division of Epidemiology and Public Health, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Ewan Cameron
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA, Australia
- Faculty of Health Sciences, Curtin University, Bentley, WA, Australia
| | - Daniel J. Weiss
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA, Australia
- Faculty of Health Sciences, Curtin University, Bentley, WA, Australia
| | - Samir Bhatt
- Department of Infectious Disease Epidemiology, Imperial College, St Mary’s Hospital, London, United Kingdom
- Department of Public Health, Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark
| | - Peter W. Gething
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA, Australia
- Faculty of Health Sciences, Curtin University, Bentley, WA, Australia
| | - Katherine E. Battle
- Institute for Disease Modeling, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America
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5
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Weiss DJ, Boyhan TF, Connell M, Alene KA, Dzianach PA, Symons TL, Vargas-Ruiz CA, Gething PW, Cameron E. Impacts on Human Movement in Australian Cities Related to the COVID-19 Pandemic. Trop Med Infect Dis 2023; 8:363. [PMID: 37505659 PMCID: PMC10385321 DOI: 10.3390/tropicalmed8070363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/04/2023] [Accepted: 07/10/2023] [Indexed: 07/29/2023] Open
Abstract
No studies have yet examined high-resolution shifts in the spatial patterns of human movement in Australia throughout 2020 and 2021, a period coincident with the repeated enactment and removal of varied governmental restrictions aimed at reducing community transmission of SARS-CoV-2. We compared overlapping timeseries of COVID-19 pandemic-related restrictions, epidemiological data on cases and vaccination rates, and high-resolution human movement data to characterize population-level responses to the pandemic in Australian cities. We found that restrictions on human movement and/or mandatory business closures reduced the average population-level weekly movement volumes in cities, as measured by aggregated travel time, by almost half. Of the movements that continued to occur, long movements reduced more dramatically than short movements, likely indicating that people stayed closer to home. We also found that the repeated lockdowns did not reduce their impact on human movement, but the effect of the restrictions on human movement waned as the duration of restrictions increased. Lastly, we found that after restrictions ceased, the subsequent surge in SARS-CoV-2 transmission coincided with a substantial, non-mandated drop in human movement volume. These findings have implications for public health policy makers when faced with anticipating responses to restrictions during future emergency situations.
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Affiliation(s)
- Daniel J Weiss
- Telethon Kids Institute, Perth Children's Hospital, Nedlands, WA 6009, Australia
- School of Population Health, Curtin University, Bentley, WA 6102, Australia
| | - Tara F Boyhan
- School of Population Health, Curtin University, Bentley, WA 6102, Australia
| | - Mark Connell
- School of Population Health, Curtin University, Bentley, WA 6102, Australia
| | - Kefyalew Addis Alene
- Telethon Kids Institute, Perth Children's Hospital, Nedlands, WA 6009, Australia
- School of Population Health, Curtin University, Bentley, WA 6102, Australia
| | - Paulina A Dzianach
- Telethon Kids Institute, Perth Children's Hospital, Nedlands, WA 6009, Australia
| | - Tasmin L Symons
- Telethon Kids Institute, Perth Children's Hospital, Nedlands, WA 6009, Australia
| | - Camilo A Vargas-Ruiz
- Telethon Kids Institute, Perth Children's Hospital, Nedlands, WA 6009, Australia
| | - Peter W Gething
- Telethon Kids Institute, Perth Children's Hospital, Nedlands, WA 6009, Australia
- School of Population Health, Curtin University, Bentley, WA 6102, Australia
| | - Ewan Cameron
- Telethon Kids Institute, Perth Children's Hospital, Nedlands, WA 6009, Australia
- School of Population Health, Curtin University, Bentley, WA 6102, Australia
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6
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Alene KA, Python A, Weiss DJ, Elagali A, Wagaw ZA, Kumsa A, Gething PW, Clements ACA. Mapping tuberculosis prevalence in Ethiopia using geospatial meta-analysis. Int J Epidemiol 2023:7159696. [PMID: 37164625 DOI: 10.1093/ije/dyad052] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 04/19/2023] [Indexed: 05/12/2023] Open
Abstract
BACKGROUND Reliable and detailed data on the prevalence of tuberculosis (TB) with sub-national estimates are scarce in Ethiopia. We address this knowledge gap by spatially predicting the national, sub-national and local prevalence of TB, and identifying drivers of TB prevalence across the country. METHODS TB prevalence data were obtained from the Ethiopia national TB prevalence survey and from a comprehensive review of published reports. Geospatial covariates were obtained from publicly available sources. A random effects meta-analysis was used to estimate a pooled prevalence of TB at the national level, and model-based geostatistics were used to estimate the spatial variation of TB prevalence at sub-national and local levels. Within the MBG Plugin Framework, a logistic regression model was fitted to TB prevalence data using both fixed covariate effects and spatial random effects to identify drivers of TB and to predict the prevalence of TB. RESULTS The overall pooled prevalence of TB in Ethiopia was 0.19% [95% confidence intervals (CI): 0.12%-0.28%]. There was a high degree of heterogeneity in the prevalence of TB (I2 96.4%, P <0.001), which varied by geographical locations, data collection periods and diagnostic methods. The highest prevalence of TB was observed in Dire Dawa (0.96%), Gambela (0.88%), Somali (0.42%), Addis Ababa (0.28%) and Afar (0.24%) regions. Nationally, there was a decline in TB prevalence from 0.18% in 2001 to 0.04% in 2009. However, prevalence increased back to 0.29% in 2014. Substantial spatial variation of TB prevalence was observed at a regional level, with a higher prevalence observed in the border regions, and at a local level within regions. The spatial distribution of TB prevalence was positively associated with population density. CONCLUSION The results of this study showed that TB prevalence varied substantially at sub-national and local levels in Ethiopia. Spatial patterns were associated with population density. These results suggest that targeted interventions in high-risk areas may reduce the burden of TB in Ethiopia and additional data collection would be required to make further inferences on TB prevalence in areas that lack data.
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Affiliation(s)
- Kefyalew Addis Alene
- School of Population Health, Faculty of Health Sciences, Curtin University, Bentley, WA, Australia
- Telethon Kids Institute, Nedlands, WA, Australia
| | - Andre Python
- Center for Data Science, Zhejiang University, Zhejiang Province, Hangzhou, China
| | - Daniel J Weiss
- School of Population Health, Faculty of Health Sciences, Curtin University, Bentley, WA, Australia
- Telethon Kids Institute, Nedlands, WA, Australia
| | | | | | - Andargachew Kumsa
- National TB Control Program, Ethiopia Ministry of Health, Addis Ababa, Ethiopia
| | - Peter W Gething
- School of Population Health, Faculty of Health Sciences, Curtin University, Bentley, WA, Australia
- Telethon Kids Institute, Nedlands, WA, Australia
| | - Archie C A Clements
- School of Population Health, Faculty of Health Sciences, Curtin University, Bentley, WA, Australia
- Telethon Kids Institute, Nedlands, WA, Australia
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7
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Bertozzi-Villa A, Bever CA, Gerardin J, Proctor JL, Wu M, Harding D, Hollingsworth TD, Bhatt S, Gething PW. An archetypes approach to malaria intervention impact mapping: a new framework and example application. Malar J 2023; 22:138. [PMID: 37101269 PMCID: PMC10131392 DOI: 10.1186/s12936-023-04535-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 03/15/2023] [Indexed: 04/28/2023] Open
Abstract
BACKGROUND As both mechanistic and geospatial malaria modeling methods become more integrated into malaria policy decisions, there is increasing demand for strategies that combine these two methods. This paper introduces a novel archetypes-based methodology for generating high-resolution intervention impact maps based on mechanistic model simulations. An example configuration of the framework is described and explored. METHODS First, dimensionality reduction and clustering techniques were applied to rasterized geospatial environmental and mosquito covariates to find archetypal malaria transmission patterns. Next, mechanistic models were run on a representative site from each archetype to assess intervention impact. Finally, these mechanistic results were reprojected onto each pixel to generate full maps of intervention impact. The example configuration used ERA5 and Malaria Atlas Project covariates, singular value decomposition, k-means clustering, and the Institute for Disease Modeling's EMOD model to explore a range of three-year malaria interventions primarily focused on vector control and case management. RESULTS Rainfall, temperature, and mosquito abundance layers were clustered into ten transmission archetypes with distinct properties. Example intervention impact curves and maps highlighted archetype-specific variation in efficacy of vector control interventions. A sensitivity analysis showed that the procedure for selecting representative sites to simulate worked well in all but one archetype. CONCLUSION This paper introduces a novel methodology which combines the richness of spatiotemporal mapping with the rigor of mechanistic modeling to create a multi-purpose infrastructure for answering a broad range of important questions in the malaria policy space. It is flexible and adaptable to a range of input covariates, mechanistic models, and mapping strategies and can be adapted to the modelers' setting of choice.
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Affiliation(s)
- Amelia Bertozzi-Villa
- Institute for Disease Modeling, Bill & Melinda Gates Foundation, Seattle, USA.
- Malaria Atlas Project, Telethon Kids Institute, Perth, Australia.
- Big Data Institute, Nuffield Department of Medicine, Oxford University, Oxford, UK.
| | - Caitlin A Bever
- Institute for Disease Modeling, Bill & Melinda Gates Foundation, Seattle, USA
| | - Jaline Gerardin
- Institute for Disease Modeling, Bill & Melinda Gates Foundation, Seattle, USA
- Department of Preventive Medicine and Institute for Global Health, Northwestern University, Chicago, USA
| | - Joshua L Proctor
- Institute for Disease Modeling, Bill & Melinda Gates Foundation, Seattle, USA
| | - Meikang Wu
- Institute for Disease Modeling, Bill & Melinda Gates Foundation, Seattle, USA
| | - Dennis Harding
- Institute for Disease Modeling, Bill & Melinda Gates Foundation, Seattle, USA
| | | | - Samir Bhatt
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College, London, UK
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Peter W Gething
- Malaria Atlas Project, Telethon Kids Institute, Perth, Australia
- Curtin University, Perth, Australia
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8
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Dzianach PA, Rumisha SF, Lubinda J, Saddler A, van den Berg M, Gelaw YA, Harris JR, Browne AJ, Sanna F, Rozier JA, Galatas B, Anderson LF, Vargas-Ruiz CA, Cameron E, Gething PW, Weiss DJ. Evaluating COVID-19-Related Disruptions to Effective Malaria Case Management in 2020-2021 and Its Potential Effects on Malaria Burden in Sub-Saharan Africa. Trop Med Infect Dis 2023; 8:216. [PMID: 37104342 PMCID: PMC10143572 DOI: 10.3390/tropicalmed8040216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 03/28/2023] [Accepted: 03/30/2023] [Indexed: 04/09/2023] Open
Abstract
The COVID-19 pandemic has led to far-reaching disruptions to health systems, including preventative and curative services for malaria. The aim of this study was to estimate the magnitude of disruptions in malaria case management in sub-Saharan Africa and their impact on malaria burden during the COVID-19 pandemic. We used survey data collected by the World Health Organization, in which individual country stakeholders reported on the extent of disruptions to malaria diagnosis and treatment. The relative disruption values were then applied to estimates of antimalarial treatment rates and used as inputs to an established spatiotemporal Bayesian geostatistical framework to generate annual malaria burden estimates with case management disruptions. This enabled an estimation of the additional malaria burden attributable to pandemic-related impacts on treatment rates in 2020 and 2021. Our analysis found that disruptions in access to antimalarial treatment in sub-Saharan Africa likely resulted in approximately 5.9 (4.4-7.2 95% CI) million more malaria cases and 76 (20-132) thousand additional deaths in the 2020-2021 period within the study region, equivalent to approximately 1.2% (0.3-2.1 95% CI) greater clinical incidence of malaria and 8.1% (2.1-14.1 95% CI) greater malaria mortality than expected in the absence of the disruptions to malaria case management. The available evidence suggests that access to antimalarials was disrupted to a significant degree and should be considered an area of focus to avoid further escalations in malaria morbidity and mortality. The results from this analysis were used to estimate cases and deaths in the World Malaria Report 2022 during the pandemic years.
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Affiliation(s)
- Paulina A. Dzianach
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA 6009, Australia
| | - Susan F. Rumisha
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA 6009, Australia
| | - Jailos Lubinda
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA 6009, Australia
| | - Adam Saddler
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA 6009, Australia
| | | | - Yalemzewod A. Gelaw
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA 6009, Australia
| | - Joseph R. Harris
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA 6009, Australia
| | - Annie J. Browne
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA 6009, Australia
| | - Francesca Sanna
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA 6009, Australia
| | - Jennifer A. Rozier
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA 6009, Australia
| | - Beatriz Galatas
- Strategic Information for Response, Global Malaria Programme, World Health Organization, 1211 Geneva, Switzerland
| | - Laura F. Anderson
- Strategic Information for Response, Global Malaria Programme, World Health Organization, 1211 Geneva, Switzerland
| | - Camilo A. Vargas-Ruiz
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA 6009, Australia
- Faculty of Health Sciences, Curtin University, Perth, WA 6102, Australia
| | - Ewan Cameron
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA 6009, Australia
- Faculty of Health Sciences, Curtin University, Perth, WA 6102, Australia
| | - Peter W. Gething
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA 6009, Australia
- Faculty of Health Sciences, Curtin University, Perth, WA 6102, Australia
| | - Daniel J. Weiss
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA 6009, Australia
- Faculty of Health Sciences, Curtin University, Perth, WA 6102, Australia
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9
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Whittaker C, Winskill P, Sinka M, Pironon S, Massey C, Weiss DJ, Nguyen M, Gething PW, Kumar A, Ghani A, Bhatt S. A novel statistical framework for exploring the population dynamics and seasonality of mosquito populations. Proc Biol Sci 2022; 289:20220089. [PMID: 35414241 PMCID: PMC9006040 DOI: 10.1098/rspb.2022.0089] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Understanding the temporal dynamics of mosquito populations underlying vector-borne disease transmission is key to optimizing control strategies. Many questions remain surrounding the drivers of these dynamics and how they vary between species-questions rarely answerable from individual entomological studies (that typically focus on a single location or species). We develop a novel statistical framework enabling identification and classification of time series with similar temporal properties, and use this framework to systematically explore variation in population dynamics and seasonality in anopheline mosquito time series catch data spanning seven species, 40 years and 117 locations across mainland India. Our analyses reveal pronounced variation in dynamics across locations and between species in the extent of seasonality and timing of seasonal peaks. However, we show that these diverse dynamics can be clustered into four 'dynamical archetypes', each characterized by distinct temporal properties and associated with a largely unique set of environmental factors. Our results highlight that a range of environmental factors including rainfall, temperature, proximity to static water bodies and patterns of land use (particularly urbanicity) shape the dynamics and seasonality of mosquito populations, and provide a generically applicable framework to better identify and understand patterns of seasonal variation in vectors relevant to public health.
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Affiliation(s)
- Charles Whittaker
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College, London, UK
| | - Peter Winskill
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College, London, UK
| | | | | | - Claire Massey
- Big Data Institute, University of Oxford, Old Road Campus, Oxford, UK
| | - Daniel J Weiss
- Malaria Atlas Project, Telethon Kids Institute, Perth Children's Hospital, Nedlands, WA 6009, Australia.,School of Public Health, Curtin University, Bentley, WA 6102, Australia
| | - Michele Nguyen
- Asian School of the Environment, Nanyang Technological University, Singapore, Singapore
| | - Peter W Gething
- Malaria Atlas Project, Telethon Kids Institute, Perth Children's Hospital, Nedlands, WA 6009, Australia.,School of Public Health, Curtin University, Bentley, WA 6102, Australia
| | - Ashwani Kumar
- Vector Control Research Centre, Indira Nagar, Puducherry, India
| | - Azra Ghani
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College, London, UK
| | - Samir Bhatt
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College, London, UK.,Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
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10
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Alene KA, Elagali A, Barth DD, Rumisha SF, Amratia P, Weiss DJ, Atalell KA, Erena AK, Gething PW, Clements ACA. Spatial codistribution of HIV, tuberculosis and malaria in Ethiopia. BMJ Glob Health 2022; 7:bmjgh-2021-007599. [PMID: 35217531 PMCID: PMC8867247 DOI: 10.1136/bmjgh-2021-007599] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 01/20/2022] [Indexed: 12/15/2022] Open
Abstract
Background HIV, tuberculosis (TB) and malaria are the three most important infectious diseases in Ethiopia, and sub-Saharan Africa. Understanding the spatial codistribution of these diseases is critical for designing geographically targeted and integrated disease control programmes. This study investigated the spatial overlap and drivers of HIV, TB and malaria prevalence in Ethiopia. Methods HIV, TB and malaria data were obtained from different nationwide prevalence surveys, and geospatial covariates were obtained from publicly available sources. A Bayesian model-based geostatistical framework was applied to each survey leveraging the strength of high-resolution spatial covariates to predict continuous disease-specific prevalence surfaces and their codistribution. Results The national prevalence was 1.54% (95% CI 1.40 to 1.70) for HIV, 0.39% (95% CI 0.34 to 0.45) for TB and 1.1% (95%CI 0.95 to 1.32) for malaria. Substantial subnational variation was predicted with the highest HIV prevalence estimated in Gambela (4.52%), Addis Ababa (3.52%) and Dire Dawa (2.67%) regions. TB prevalence was highest in Dire Dawa (0.96%) and Gambela (0.88%), while malaria was highest in Gambela (6.1%) and Benishangul-Gumuz (3.8%). Spatial overlap of their prevalence was observed in some parts of the country, mainly Gambela region. Spatial distribution of the diseases was significantly associated with healthcare access, demographic, and climatic factors. Conclusions The national distribution of HIV, TB and malaria was highly focal in Ethiopia, with substantial variation at subnational and local levels. Spatial distribution of the diseases was significantly associated with healthcare access, demographic and climatic factors. Spatial overlap of HIV, TB and malaria prevalence was observed in some parts of the country. Integrated control programmes for these diseases should be targeted to these areas with high levels of co-endemicity.
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Affiliation(s)
- Kefyalew Addis Alene
- Geospatial Health and Development, Telethon Kids Institute, Nedlands, Western Australia, Australia .,Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia
| | - Ahmed Elagali
- Geospatial Health and Development, Telethon Kids Institute, Nedlands, Western Australia, Australia
| | - Dylan D Barth
- University of Western Australia, Perth, Western Australia, Australia.,Wesfarmers Centre for Vaccines and Infectious Diseases, Telethon Kids Institute, Nedlands, Western Australia, Australia
| | - Susan F Rumisha
- Geospatial Health and Development, Telethon Kids Institute, Nedlands, Western Australia, Australia.,National Institute for Medical Research, Dar es Salaam, Tanzania
| | - Punam Amratia
- Geospatial Health and Development, Telethon Kids Institute, Nedlands, Western Australia, Australia
| | - Daniel J Weiss
- Geospatial Health and Development, Telethon Kids Institute, Nedlands, Western Australia, Australia.,Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia
| | - Kendalem Asmare Atalell
- School of Nursing, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | | | - Peter W Gething
- Geospatial Health and Development, Telethon Kids Institute, Nedlands, Western Australia, Australia.,Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia
| | - Archie C A Clements
- Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia
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11
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Arambepola R, Lucas TCD, Nandi AK, Gething PW, Cameron E. A simulation study of disaggregation regression for spatial disease mapping. Stat Med 2021; 41:1-16. [PMID: 34658042 DOI: 10.1002/sim.9220] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 07/30/2021] [Accepted: 09/20/2021] [Indexed: 11/07/2022]
Abstract
Disaggregation regression has become an important tool in spatial disease mapping for making fine-scale predictions of disease risk from aggregated response data. By including high resolution covariate information and modeling the data generating process on a fine scale, it is hoped that these models can accurately learn the relationships between covariates and response at a fine spatial scale. However, validating these high resolution predictions can be a challenge, as often there is no data observed at this spatial scale. In this study, disaggregation regression was performed on simulated data in various settings and the resulting fine-scale predictions are compared to the simulated ground truth. Performance was investigated with varying numbers of data points, sizes of aggregated areas and levels of model misspecification. The effectiveness of cross validation on the aggregate level as a measure of fine-scale predictive performance was also investigated. Predictive performance improved as the number of observations increased and as the size of the aggregated areas decreased. When the model was well-specified, fine-scale predictions were accurate even with small numbers of observations and large aggregated areas. Under model misspecification predictive performance was significantly worse for large aggregated areas but remained high when response data was aggregated over smaller regions. Cross-validation correlation on the aggregate level was a moderately good predictor of fine-scale predictive performance. While these simulations are unlikely to capture the nuances of real-life response data, this study gives insight into the effectiveness of disaggregation regression in different contexts.
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Affiliation(s)
- Rohan Arambepola
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Tim C D Lucas
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Anita K Nandi
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Peter W Gething
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.,Telethon Kids Institute, Perth Children's Hospital, Perth, Western Australia, Australia.,School of Public Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia
| | - Ewan Cameron
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.,Telethon Kids Institute, Perth Children's Hospital, Perth, Western Australia, Australia.,School of Public Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia
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12
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Cameron E, Young AJ, Twohig KA, Pothin E, Bhavnani D, Dismer A, Merilien JB, Hamre K, Meyer P, Le Menach A, Cohen JM, Marseille S, Lemoine JF, Telfort MA, Chang MA, Won K, Knipes A, Rogier E, Amratia P, Weiss DJ, Gething PW, Battle KE. Mapping the endemicity and seasonality of clinical malaria for intervention targeting in Haiti using routine case data. eLife 2021; 10:62122. [PMID: 34058123 PMCID: PMC8169118 DOI: 10.7554/elife.62122] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 05/15/2021] [Indexed: 01/26/2023] Open
Abstract
Towards the goal of malaria elimination on Hispaniola, the National Malaria Control Program of Haiti and its international partner organisations are conducting a campaign of interventions targeted to high-risk communities prioritised through evidence-based planning. Here we present a key piece of this planning: an up-to-date, fine-scale endemicity map and seasonality profile for Haiti informed by monthly case counts from 771 health facilities reporting from across the country throughout the 6-year period from January 2014 to December 2019. To this end, a novel hierarchical Bayesian modelling framework was developed in which a latent, pixel-level incidence surface with spatio-temporal innovations is linked to the observed case data via a flexible catchment sub-model designed to account for the absence of data on case household locations. These maps have focussed the delivery of indoor residual spraying and focal mass drug administration in the Grand’Anse Department in South-Western Haiti.
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Affiliation(s)
- Ewan Cameron
- Curtin University, Perth, Australia.,Telethon Kids Institute, Perth Children's Hospital, Perth, Australia
| | - Alyssa J Young
- Clinton Health Access Initiative, Boston, United States.,Tulane University School of Public Health and Tropical Medicine, New Orleans, United States
| | - Katherine A Twohig
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Emilie Pothin
- Clinton Health Access Initiative, Boston, United States.,Swiss Tropical and Public Health Institute, Basel, Switzerland
| | | | - Amber Dismer
- Division of Global Health Protection, Centers for Disease Control and Prevention, Atlanta, United States
| | | | - Karen Hamre
- Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, United States
| | - Phoebe Meyer
- Clinton Health Access Initiative, Boston, United States
| | | | | | - Samson Marseille
- Programme National de Contrôle de la Malaria/MSPP, Port-au-Prince, Haiti.,Direction d'Epidémiologie de Laboratoire et de la Recherche, Port-au-Prince, Haiti
| | | | | | - Michelle A Chang
- Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, United States
| | - Kimberly Won
- Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, United States
| | - Alaine Knipes
- Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, United States
| | - Eric Rogier
- Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, United States
| | - Punam Amratia
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Daniel J Weiss
- Curtin University, Perth, Australia.,Telethon Kids Institute, Perth Children's Hospital, Perth, Australia
| | - Peter W Gething
- Curtin University, Perth, Australia.,Telethon Kids Institute, Perth Children's Hospital, Perth, Australia
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13
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Devine A, Battle KE, Meagher N, Howes RE, Dini S, Gething PW, Simpson JA, Price RN, Lubell Y. Global economic costs due to vivax malaria and the potential impact of its radical cure: A modelling study. PLoS Med 2021; 18:e1003614. [PMID: 34061843 PMCID: PMC8168905 DOI: 10.1371/journal.pmed.1003614] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 04/07/2021] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND In 2017, an estimated 14 million cases of Plasmodium vivax malaria were reported from Asia, Central and South America, and the Horn of Africa. The clinical burden of vivax malaria is largely driven by its ability to form dormant liver stages (hypnozoites) that can reactivate to cause recurrent episodes of malaria. Elimination of both the blood and liver stages of the parasites ("radical cure") is required to achieve a sustained clinical response and prevent ongoing transmission of the parasite. Novel treatment options and point-of-care diagnostics are now available to ensure that radical cure can be administered safely and effectively. We quantified the global economic cost of vivax malaria and estimated the potential cost benefit of a policy of radical cure after testing patients for glucose-6-phosphate dehydrogenase (G6PD) deficiency. METHODS AND FINDINGS Estimates of the healthcare provider and household costs due to vivax malaria were collated and combined with national case estimates for 44 endemic countries in 2017. These provider and household costs were compared with those that would be incurred under 2 scenarios for radical cure following G6PD screening: (1) complete adherence following daily supervised primaquine therapy and (2) unsupervised treatment with an assumed 40% effectiveness. A probabilistic sensitivity analysis generated credible intervals (CrIs) for the estimates. Globally, the annual cost of vivax malaria was US$359 million (95% CrI: US$222 to 563 million), attributable to 14.2 million cases of vivax malaria in 2017. From a societal perspective, adopting a policy of G6PD deficiency screening and supervision of primaquine to all eligible patients would prevent 6.1 million cases and reduce the global cost of vivax malaria to US$266 million (95% CrI: US$161 to 415 million), although healthcare provider costs would increase by US$39 million. If perfect adherence could be achieved with a single visit, then the global cost would fall further to US$225 million, equivalent to $135 million in cost savings from the baseline global costs. A policy of unsupervised primaquine reduced the cost to US$342 million (95% CrI: US$209 to 532 million) while preventing 2.1 million cases. Limitations of the study include partial availability of country-level cost data and parameter uncertainty for the proportion of patients prescribed primaquine, patient adherence to a full course of primaquine, and effectiveness of primaquine when unsupervised. CONCLUSIONS Our modelling study highlights a substantial global economic burden of vivax malaria that could be reduced through investment in safe and effective radical cure achieved by routine screening for G6PD deficiency and supervision of treatment. Novel, low-cost interventions for improving adherence to primaquine to ensure effective radical cure and widespread access to screening for G6PD deficiency will be critical to achieving the timely global elimination of P. vivax.
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Affiliation(s)
- Angela Devine
- Division of Global and Tropical Health, Menzies School of Health Research and Charles Darwin University, Darwin, Northern Territory, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Katherine E. Battle
- Institute for Disease Modeling, Seattle, Washington, United States of America
| | - Niamh Meagher
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia
- Victorian Infectious Diseases Reference Laboratory Epidemiology Unit, Royal Melbourne Hospital, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Rosalind E. Howes
- Foundation for Innovative New Diagnostics (FIND), Geneva, Switzerland
- Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Saber Dini
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Peter W. Gething
- Telethon Kids Institute, Perth Children’s Hospital, Nedlands, Western Australia, Australia
- Curtin University, Bentley, Western Australia, Australia
| | - Julie A. Simpson
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Ric N. Price
- Division of Global and Tropical Health, Menzies School of Health Research and Charles Darwin University, Darwin, Northern Territory, Australia
- Nuffield Department of Medicine, Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
- Mahidol Oxford Tropical Medicine Research Unit, Bangkok, Thailand
| | - Yoel Lubell
- Nuffield Department of Medicine, Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
- Mahidol Oxford Tropical Medicine Research Unit, Bangkok, Thailand
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14
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Lucas TCD, Nandi AK, Chestnutt EG, Twohig KA, Keddie SH, Collins EL, Howes RE, Nguyen M, Rumisha SF, Python A, Arambepola R, Bertozzi‐Villa A, Hancock P, Amratia P, Battle KE, Cameron E, Gething PW, Weiss DJ. Mapping malaria by sharing spatial information between incidence and prevalence data sets. J R Stat Soc Ser C Appl Stat 2021. [DOI: 10.1111/rssc.12484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
| | | | | | | | | | | | | | | | | | - Andre Python
- Big Data Institute University of Oxford Oxford UK
| | | | - Amelia Bertozzi‐Villa
- Big Data Institute University of Oxford Oxford UK
- Institute for Disease Modeling Bellevue Washington USA
| | | | | | | | - Ewan Cameron
- Big Data Institute University of Oxford Oxford UK
| | - Peter W. Gething
- Big Data Institute University of Oxford Oxford UK
- Telethon Kids Institute Perth Children's Hospital Perth Australia
- Curtin University Perth Australia
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15
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Weiss DJ, Bertozzi-Villa A, Rumisha SF, Amratia P, Arambepola R, Battle KE, Cameron E, Chestnutt E, Gibson HS, Harris J, Keddie S, Millar JJ, Rozier J, Symons TL, Vargas-Ruiz C, Hay SI, Smith DL, Alonso PL, Noor AM, Bhatt S, Gething PW. Indirect effects of the COVID-19 pandemic on malaria intervention coverage, morbidity, and mortality in Africa: a geospatial modelling analysis. Lancet Infect Dis 2021; 21:59-69. [PMID: 32971006 PMCID: PMC7505634 DOI: 10.1016/s1473-3099(20)30700-3] [Citation(s) in RCA: 117] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 08/12/2020] [Accepted: 08/12/2020] [Indexed: 02/07/2023]
Abstract
BACKGROUND Substantial progress has been made in reducing the burden of malaria in Africa since 2000, but those gains could be jeopardised if the COVID-19 pandemic affects the availability of key malaria control interventions. The aim of this study was to evaluate plausible effects on malaria incidence and mortality under different levels of disruption to malaria control. METHODS Using an established set of spatiotemporal Bayesian geostatistical models, we generated geospatial estimates across malaria-endemic African countries of the clinical case incidence and mortality of malaria, incorporating an updated database of parasite rate surveys, insecticide-treated net (ITN) coverage, and effective treatment rates. We established a baseline estimate for the anticipated malaria burden in Africa in the absence of COVID-19-related disruptions, and repeated the analysis for nine hypothetical scenarios in which effective treatment with an antimalarial drug and distribution of ITNs (both through routine channels and mass campaigns) were reduced to varying extents. FINDINGS We estimated 215·2 (95% uncertainty interval 143·7-311·6) million cases and 386·4 (307·8-497·8) thousand deaths across malaria-endemic African countries in 2020 in our baseline scenario of undisrupted intervention coverage. With greater reductions in access to effective antimalarial drug treatment, our model predicted increasing numbers of cases and deaths: 224·1 (148·7-326·8) million cases and 487·9 (385·3-634·6) thousand deaths with a 25% reduction in antimalarial drug coverage; 233·1 (153·7-342·5) million cases and 597·4 (468·0-784·4) thousand deaths with a 50% reduction; and 242·3 (158·7-358·8) million cases and 715·2 (556·4-947·9) thousand deaths with a 75% reduction. Halting planned 2020 ITN mass distribution campaigns and reducing routine ITN distributions by 25%-75% also increased malaria burden to a total of 230·5 (151·6-343·3) million cases and 411·7 (322·8-545·5) thousand deaths with a 25% reduction; 232·8 (152·3-345·9) million cases and 415·5 (324·3-549·4) thousand deaths with a 50% reduction; and 234·0 (152·9-348·4) million cases and 417·6 (325·5-553·1) thousand deaths with a 75% reduction. When ITN coverage and antimalarial drug coverage were synchronously reduced, malaria burden increased to 240·5 (156·5-358·2) million cases and 520·9 (404·1-691·9) thousand deaths with a 25% reduction; 251·0 (162·2-377·0) million cases and 640·2 (492·0-856·7) thousand deaths with a 50% reduction; and 261·6 (167·7-396·8) million cases and 768·6 (586·1-1038·7) thousand deaths with a 75% reduction. INTERPRETATION Under pessimistic scenarios, COVID-19-related disruption to malaria control in Africa could almost double malaria mortality in 2020, and potentially lead to even greater increases in subsequent years. To avoid a reversal of two decades of progress against malaria, averting this public health disaster must remain an integrated priority alongside the response to COVID-19. FUNDING Bill and Melinda Gates Foundation; Channel 7 Telethon Trust, Western Australia.
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Affiliation(s)
- Daniel J Weiss
- Telethon Kids Institute, Perth Children's Hospital, Perth, WA, Australia; Curtin University, Perth, WA, Australia; Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Amelia Bertozzi-Villa
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK; Institute for Disease Modeling, Bellevue, WA, USA
| | - Susan F Rumisha
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK; National Institute for Medical Research, Dar es Salaam, Tanzania
| | - Punam Amratia
- Telethon Kids Institute, Perth Children's Hospital, Perth, WA, Australia
| | - Rohan Arambepola
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | | | - Ewan Cameron
- Telethon Kids Institute, Perth Children's Hospital, Perth, WA, Australia; Curtin University, Perth, WA, Australia
| | - Elisabeth Chestnutt
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Harry S Gibson
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Joseph Harris
- Telethon Kids Institute, Perth Children's Hospital, Perth, WA, Australia
| | - Suzanne Keddie
- Telethon Kids Institute, Perth Children's Hospital, Perth, WA, Australia
| | - Justin J Millar
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Jennifer Rozier
- Telethon Kids Institute, Perth Children's Hospital, Perth, WA, Australia
| | - Tasmin L Symons
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Camilo Vargas-Ruiz
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Simon I Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - David L Smith
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Pedro L Alonso
- Global Malaria Programme, World Health Organization, Geneva, Switzerland
| | - Abdisalan M Noor
- Global Malaria Programme, World Health Organization, Geneva, Switzerland
| | - Samir Bhatt
- Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Peter W Gething
- Telethon Kids Institute, Perth Children's Hospital, Perth, WA, Australia; Curtin University, Perth, WA, Australia.
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16
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Arambepola R, Keddie SH, Collins EL, Twohig KA, Amratia P, Bertozzi-Villa A, Chestnutt EG, Harris J, Millar J, Rozier J, Rumisha SF, Symons TL, Vargas-Ruiz C, Andriamananjara M, Rabeherisoa S, Ratsimbasoa AC, Howes RE, Weiss DJ, Gething PW, Cameron E. Spatiotemporal mapping of malaria prevalence in Madagascar using routine surveillance and health survey data. Sci Rep 2020; 10:18129. [PMID: 33093622 PMCID: PMC7581764 DOI: 10.1038/s41598-020-75189-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 10/12/2020] [Indexed: 11/16/2022] Open
Abstract
Malaria transmission in Madagascar is highly heterogeneous, exhibiting spatial, seasonal and long-term trends. Previous efforts to map malaria risk in Madagascar used prevalence data from Malaria Indicator Surveys. These cross-sectional surveys, conducted during the high transmission season most recently in 2013 and 2016, provide nationally representative prevalence data but cover relatively short time frames. Conversely, monthly case data are collected at health facilities but suffer from biases, including incomplete reporting and low rates of treatment seeking. We combined survey and case data to make monthly maps of prevalence between 2013 and 2016. Health facility catchment populations were estimated to produce incidence rates from the case data. Smoothed incidence surfaces, environmental and socioeconomic covariates, and survey data informed a Bayesian prevalence model, in which a flexible incidence-to-prevalence relationship was learned. Modelled spatial trends were consistent over time, with highest prevalence in the coastal regions and low prevalence in the highlands and desert south. Prevalence was lowest in 2014 and peaked in 2015 and seasonality was widely observed, including in some lower transmission regions. These trends highlight the utility of monthly prevalence estimates over the four year period. By combining survey and case data using this two-step modelling approach, we were able to take advantage of the relative strengths of each metric while accounting for potential bias in the case data. Similar modelling approaches combining large datasets of different malaria metrics may be applicable across sub-Saharan Africa.
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Affiliation(s)
- Rohan Arambepola
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
| | - Suzanne H Keddie
- Telethon Kids Institute, Perth Children's Hospital, Perth, Australia
| | - Emma L Collins
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Katherine A Twohig
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Punam Amratia
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Amelia Bertozzi-Villa
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Institute for Disease Modeling, Bellevue, WA, USA
| | - Elisabeth G Chestnutt
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Joseph Harris
- Telethon Kids Institute, Perth Children's Hospital, Perth, Australia
| | - Justin Millar
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Jennifer Rozier
- Telethon Kids Institute, Perth Children's Hospital, Perth, Australia
| | - Susan F Rumisha
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Tasmin L Symons
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Camilo Vargas-Ruiz
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Mauricette Andriamananjara
- Programme National de Lutte contre le Paludisme, Antananarivo, Madagascar
- Ministère de Santé Publique, Antananarivo, Madagascar
| | - Saraha Rabeherisoa
- Programme National de Lutte contre le Paludisme, Antananarivo, Madagascar
| | - Arsène C Ratsimbasoa
- Programme National de Lutte contre le Paludisme, Antananarivo, Madagascar
- University of Fianarantsoa, Fianarantsoa, Madagascar
| | - Rosalind E Howes
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Foundation for Innovative New Diagnostics, Geneva, Switzerland
| | - Daniel J Weiss
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Telethon Kids Institute, Perth Children's Hospital, Perth, Australia
- Curtin University, Perth, Australia
| | - Peter W Gething
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Telethon Kids Institute, Perth Children's Hospital, Perth, Australia
- Curtin University, Perth, Australia
| | - Ewan Cameron
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Telethon Kids Institute, Perth Children's Hospital, Perth, Australia
- Curtin University, Perth, Australia
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17
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Rathmes G, Rumisha SF, Lucas TCD, Twohig KA, Python A, Nguyen M, Nandi AK, Keddie SH, Collins EL, Rozier JA, Gibson HS, Chestnutt EG, Battle KE, Humphreys GS, Amratia P, Arambepola R, Bertozzi-Villa A, Hancock P, Millar JJ, Symons TL, Bhatt S, Cameron E, Guerin PJ, Gething PW, Weiss DJ. Global estimation of anti-malarial drug effectiveness for the treatment of uncomplicated Plasmodium falciparum malaria 1991-2019. Malar J 2020; 19:374. [PMID: 33081784 PMCID: PMC7573874 DOI: 10.1186/s12936-020-03446-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 10/10/2020] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Anti-malarial drugs play a critical role in reducing malaria morbidity and mortality, but their role is mediated by their effectiveness. Effectiveness is defined as the probability that an anti-malarial drug will successfully treat an individual infected with malaria parasites under routine health care delivery system. Anti-malarial drug effectiveness (AmE) is influenced by drug resistance, drug quality, health system quality, and patient adherence to drug use; its influence on malaria burden varies through space and time. METHODS This study uses data from 232 efficacy trials comprised of 86,776 infected individuals to estimate the artemisinin-based and non-artemisinin-based AmE for treating falciparum malaria between 1991 and 2019. Bayesian spatiotemporal models were fitted and used to predict effectiveness at the pixel-level (5 km × 5 km). The median and interquartile ranges (IQR) of AmE are presented for all malaria-endemic countries. RESULTS The global effectiveness of artemisinin-based drugs was 67.4% (IQR: 33.3-75.8), 70.1% (43.6-76.0) and 71.8% (46.9-76.4) for the 1991-2000, 2006-2010, and 2016-2019 periods, respectively. Countries in central Africa, a few in South America, and in the Asian region faced the challenge of lower effectiveness of artemisinin-based anti-malarials. However, improvements were seen after 2016, leaving only a few hotspots in Southeast Asia where resistance to artemisinin and partner drugs is currently problematic and in the central Africa where socio-demographic challenges limit effectiveness. The use of artemisinin-based combination therapy (ACT) with a competent partner drug and having multiple ACT as first-line treatment choice sustained high levels of effectiveness. High levels of access to healthcare, human resource capacity, education, and proximity to cities were associated with increased effectiveness. Effectiveness of non-artemisinin-based drugs was much lower than that of artemisinin-based with no improvement over time: 52.3% (17.9-74.9) for 1991-2000 and 55.5% (27.1-73.4) for 2011-2015. Overall, AmE for artemisinin-based and non-artemisinin-based drugs were, respectively, 29.6 and 36% below clinical efficacy as measured in anti-malarial drug trials. CONCLUSIONS This study provides evidence that health system performance, drug quality and patient adherence influence the effectiveness of anti-malarials used in treating uncomplicated falciparum malaria. These results provide guidance to countries' treatment practises and are critical inputs for malaria prevalence and incidence models used to estimate national level malaria burden.
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Affiliation(s)
- Giulia Rathmes
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Susan F Rumisha
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Telethon Kids Institute, Perth, Australia.
| | - Tim C D Lucas
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Katherine A Twohig
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Andre Python
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Center for Data Science, Zhejiang University, Hangzhou, 310058, China
| | - Michele Nguyen
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Anita K Nandi
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Suzanne H Keddie
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Emma L Collins
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Jennifer A Rozier
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Harry S Gibson
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Elisabeth G Chestnutt
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Katherine E Battle
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Georgina S Humphreys
- WorldWide Anti-Malarial Resistance Network (WWARN), Oxford, UK
- Infectious Diseases Data Observatory (IDDO), Oxford, UK
- Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| | - Punam Amratia
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Rohan Arambepola
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Amelia Bertozzi-Villa
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Institute for Disease Modeling, Bellevue, WA, USA
| | - Penelope Hancock
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Justin J Millar
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Tasmin L Symons
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | | | - Ewan Cameron
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Telethon Kids Institute, Perth, Australia
- Curtin University, Perth, Australia
| | - Philippe J Guerin
- WorldWide Anti-Malarial Resistance Network (WWARN), Oxford, UK
- Infectious Diseases Data Observatory (IDDO), Oxford, UK
- Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| | - Peter W Gething
- Telethon Kids Institute, Perth, Australia
- Curtin University, Perth, Australia
| | - Daniel J Weiss
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Telethon Kids Institute, Perth, Australia
- Curtin University, Perth, Australia
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Vos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, Abbasifard M, Abbasi-Kangevari M, Abbastabar H, Abd-Allah F, Abdelalim A, Abdollahi M, Abdollahpour I, Abolhassani H, Aboyans V, Abrams EM, Abreu LG, Abrigo MRM, Abu-Raddad LJ, Abushouk AI, Acebedo A, Ackerman IN, Adabi M, Adamu AA, Adebayo OM, Adekanmbi V, Adelson JD, Adetokunboh OO, Adham D, Afshari M, Afshin A, Agardh EE, Agarwal G, Agesa KM, Aghaali M, Aghamir SMK, Agrawal A, Ahmad T, Ahmadi A, Ahmadi M, Ahmadieh H, Ahmadpour E, Akalu TY, Akinyemi RO, Akinyemiju T, Akombi B, Al-Aly Z, Alam K, Alam N, Alam S, Alam T, Alanzi TM, Albertson SB, Alcalde-Rabanal JE, Alema NM, Ali M, Ali S, Alicandro G, Alijanzadeh M, Alinia C, Alipour V, Aljunid SM, Alla F, Allebeck P, Almasi-Hashiani A, Alonso J, Al-Raddadi RM, Altirkawi KA, Alvis-Guzman N, Alvis-Zakzuk NJ, Amini S, Amini-Rarani M, Aminorroaya A, Amiri F, Amit AML, Amugsi DA, Amul GGH, Anderlini D, Andrei CL, Andrei T, Anjomshoa M, Ansari F, Ansari I, Ansari-Moghaddam A, Antonio CAT, Antony CM, Antriyandarti E, Anvari D, Anwer R, Arabloo J, Arab-Zozani M, Aravkin AY, Ariani F, Ärnlöv J, Aryal KK, Arzani A, Asadi-Aliabadi M, Asadi-Pooya AA, Asghari B, Ashbaugh C, Atnafu DD, Atre SR, Ausloos F, Ausloos M, Ayala Quintanilla BP, Ayano G, Ayanore MA, Aynalem YA, Azari S, Azarian G, Azene ZN, Babaee E, Badawi A, Bagherzadeh M, Bakhshaei MH, Bakhtiari A, Balakrishnan S, Balalla S, Balassyano S, Banach M, Banik PC, Bannick MS, Bante AB, Baraki AG, Barboza MA, Barker-Collo SL, Barthelemy CM, Barua L, Barzegar A, Basu S, Baune BT, Bayati M, Bazmandegan G, Bedi N, Beghi E, Béjot Y, Bello AK, Bender RG, Bennett DA, Bennitt FB, Bensenor IM, Benziger CP, Berhe K, Bernabe E, Bertolacci GJ, Bhageerathy R, Bhala N, Bhandari D, Bhardwaj P, Bhattacharyya K, Bhutta ZA, Bibi S, Biehl MH, Bikbov B, Bin Sayeed MS, Biondi A, Birihane BM, Bisanzio D, Bisignano C, Biswas RK, Bohlouli S, Bohluli M, Bolla SRR, Boloor A, Boon-Dooley AS, Borges G, Borzì AM, Bourne R, Brady OJ, Brauer M, Brayne C, Breitborde NJK, Brenner H, Briant PS, Briggs AM, Briko NI, Britton GB, Bryazka D, Buchbinder R, Bumgarner BR, Busse R, Butt ZA, Caetano dos Santos FL, Cámera LLAA, Campos-Nonato IR, Car J, Cárdenas R, Carreras G, Carrero JJ, Carvalho F, Castaldelli-Maia JM, Castañeda-Orjuela CA, Castelpietra G, Castle CD, Castro F, Catalá-López F, Causey K, Cederroth CR, Cercy KM, Cerin E, Chandan JS, Chang AR, Charlson FJ, Chattu VK, Chaturvedi S, Chimed-Ochir O, Chin KL, Cho DY, Christensen H, Chu DT, Chung MT, Cicuttini FM, Ciobanu LG, Cirillo M, Collins EL, Compton K, Conti S, Cortesi PA, Costa VM, Cousin E, Cowden RG, Cowie BC, Cromwell EA, Cross DH, Crowe CS, Cruz JA, Cunningham M, Dahlawi SMA, Damiani G, Dandona L, Dandona R, Darwesh AM, Daryani A, Das JK, Das Gupta R, das Neves J, Dávila-Cervantes CA, Davletov K, De Leo D, Dean FE, DeCleene NK, Deen A, Degenhardt L, Dellavalle RP, Demeke FM, Demsie DG, Denova-Gutiérrez E, Dereje ND, Dervenis N, Desai R, Desalew A, Dessie GA, Dharmaratne SD, Dhungana GP, Dianatinasab M, Diaz D, Dibaji Forooshani ZS, Dingels ZV, Dirac MA, Djalalinia S, Do HT, Dokova K, Dorostkar F, Doshi CP, Doshmangir L, Douiri A, Doxey MC, Driscoll TR, Dunachie SJ, Duncan BB, Duraes AR, Eagan AW, Ebrahimi Kalan M, Edvardsson D, Ehrlich JR, El Nahas N, El Sayed I, El Tantawi M, Elbarazi I, Elgendy IY, Elhabashy HR, El-Jaafary SI, Elyazar IRF, Emamian MH, Emmons-Bell S, Erskine HE, Eshrati B, Eskandarieh S, Esmaeilnejad S, Esmaeilzadeh F, Esteghamati A, Estep K, Etemadi A, Etisso AE, Farahmand M, Faraj A, Fareed M, Faridnia R, Farinha CSES, Farioli A, Faro A, Faruque M, Farzadfar F, Fattahi N, Fazlzadeh M, Feigin VL, Feldman R, Fereshtehnejad SM, Fernandes E, Ferrari AJ, Ferreira ML, Filip I, Fischer F, Fisher JL, Fitzgerald R, Flohr C, Flor LS, Foigt NA, Folayan MO, Force LM, Fornari C, Foroutan M, Fox JT, Freitas M, Fu W, Fukumoto T, Furtado JM, Gad MM, Gakidou E, Galles NC, Gallus S, Gamkrelidze A, Garcia-Basteiro AL, Gardner WM, Geberemariyam BS, Gebrehiwot AM, Gebremedhin KB, Gebreslassie AAAA, Gershberg Hayoon A, Gething PW, Ghadimi M, Ghadiri K, Ghafourifard M, Ghajar A, Ghamari F, Ghashghaee A, Ghiasvand H, Ghith N, Gholamian A, Gilani SA, Gill PS, Gitimoghaddam M, Giussani G, Goli S, Gomez RS, Gopalani SV, Gorini G, Gorman TM, Gottlich HC, Goudarzi H, Goulart AC, Goulart BNG, Grada A, Grivna M, Grosso G, Gubari MIM, Gugnani HC, Guimaraes ALS, Guimarães RA, Guled RA, Guo G, Guo Y, Gupta R, Haagsma JA, Haddock B, Hafezi-Nejad N, Hafiz A, Hagins H, Haile LM, Hall BJ, Halvaei I, Hamadeh RR, Hamagharib Abdullah K, Hamilton EB, Han C, Han H, Hankey GJ, Haro JM, Harvey JD, Hasaballah AI, Hasanzadeh A, Hashemian M, Hassanipour S, Hassankhani H, Havmoeller RJ, Hay RJ, Hay SI, Hayat K, Heidari B, Heidari G, Heidari-Soureshjani R, Hendrie D, Henrikson HJ, Henry NJ, Herteliu C, Heydarpour F, Hird TR, Hoek HW, Hole MK, Holla R, Hoogar P, Hosgood HD, Hosseinzadeh M, Hostiuc M, Hostiuc S, Househ M, Hoy DG, Hsairi M, Hsieh VCR, Hu G, Huda TM, Hugo FN, Huynh CK, Hwang BF, Iannucci VC, Ibitoye SE, Ikuta KS, Ilesanmi OS, Ilic IM, Ilic MD, Inbaraj LR, Ippolito H, Irvani SSN, Islam MM, Islam M, Islam SMS, Islami F, Iso H, Ivers RQ, Iwu CCD, Iyamu IO, Jaafari J, Jacobsen KH, Jadidi-Niaragh F, Jafari H, Jafarinia M, Jahagirdar D, Jahani MA, Jahanmehr N, Jakovljevic M, Jalali A, Jalilian F, James SL, Janjani H, Janodia MD, Jayatilleke AU, Jeemon P, Jenabi E, Jha RP, Jha V, Ji JS, Jia P, John O, John-Akinola YO, Johnson CO, Johnson SC, Jonas JB, Joo T, Joshi A, Jozwiak JJ, Jürisson M, Kabir A, Kabir Z, Kalani H, Kalani R, Kalankesh LR, Kalhor R, Kamiab Z, Kanchan T, Karami Matin B, Karch A, Karim MA, Karimi SE, Kassa GM, Kassebaum NJ, Katikireddi SV, Kawakami N, Kayode GA, Keddie SH, Keller C, Kereselidze M, Khafaie MA, Khalid N, Khan M, Khatab K, Khater MM, Khatib MN, Khayamzadeh M, Khodayari MT, Khundkar R, Kianipour N, Kieling C, Kim D, Kim YE, Kim YJ, Kimokoti RW, Kisa A, Kisa S, Kissimova-Skarbek K, Kivimäki M, Kneib CJ, Knudsen AKS, Kocarnik JM, Kolola T, Kopec JA, Kosen S, Koul PA, Koyanagi A, Kravchenko MA, Krishan K, Krohn KJ, Kuate Defo B, Kucuk Bicer B, Kumar GA, Kumar M, Kumar P, Kumar V, Kumaresh G, Kurmi OP, Kusuma D, Kyu HH, La Vecchia C, Lacey B, Lal DK, Lalloo R, Lam JO, Lami FH, Landires I, Lang JJ, Lansingh VC, Larson SL, Larsson AO, Lasrado S, Lassi ZS, Lau KMM, Lavados PM, Lazarus JV, Ledesma JR, Lee PH, Lee SWH, LeGrand KE, Leigh J, Leonardi M, Lescinsky H, Leung J, Levi M, Lewington S, Li S, Lim LL, Lin C, Lin RT, Linehan C, Linn S, Liu HC, Liu S, Liu Z, Looker KJ, Lopez AD, Lopukhov PD, Lorkowski S, Lotufo PA, Lucas TCD, Lugo A, Lunevicius R, Lyons RA, Ma J, MacLachlan JH, Maddison ER, Maddison R, Madotto F, Mahasha PW, Mai HT, Majeed A, Maled V, Maleki S, Malekzadeh R, Malta DC, Mamun AA, Manafi A, Manafi N, Manguerra H, Mansouri B, Mansournia MA, Mantilla Herrera AM, Maravilla JC, Marks A, Martins-Melo FR, Martopullo I, Masoumi SZ, Massano J, Massenburg BB, Mathur MR, Maulik PK, McAlinden C, McGrath JJ, McKee M, Mehndiratta MM, Mehri F, Mehta KM, Meitei WB, Memiah PTN, Mendoza W, Menezes RG, Mengesha EW, Mengesha MB, Mereke A, Meretoja A, Meretoja TJ, Mestrovic T, Miazgowski B, Miazgowski T, Michalek IM, Mihretie KM, Miller TR, Mills EJ, Mirica A, Mirrakhimov EM, Mirzaei H, Mirzaei M, Mirzaei-Alavijeh M, Misganaw AT, Mithra P, Moazen B, Moghadaszadeh M, Mohamadi E, Mohammad DK, Mohammad Y, Mohammad Gholi Mezerji N, Mohammadian-Hafshejani A, Mohammadifard N, Mohammadpourhodki R, Mohammed S, Mokdad AH, Molokhia M, Momen NC, Monasta L, Mondello S, Mooney MD, Moosazadeh M, Moradi G, Moradi M, Moradi-Lakeh M, Moradzadeh R, Moraga P, Morales L, Morawska L, Moreno Velásquez I, Morgado-da-Costa J, Morrison SD, Mosser JF, Mouodi S, Mousavi SM, Mousavi Khaneghah A, Mueller UO, Munro SB, Muriithi MK, Musa KI, Muthupandian S, Naderi M, Nagarajan AJ, Nagel G, Naghshtabrizi B, Nair S, Nandi AK, Nangia V, Nansseu JR, Nayak VC, Nazari J, Negoi I, Negoi RI, Netsere HBN, Ngunjiri JW, Nguyen CT, Nguyen J, Nguyen M, Nguyen M, Nichols E, Nigatu D, Nigatu YT, Nikbakhsh R, Nixon MR, Nnaji CA, Nomura S, Norrving B, Noubiap JJ, Nowak C, Nunez-Samudio V, Oţoiu A, Oancea B, Odell CM, Ogbo FA, Oh IH, Okunga EW, Oladnabi M, Olagunju AT, Olusanya BO, Olusanya JO, Oluwasanu MM, Omar Bali A, Omer MO, Ong KL, Onwujekwe OE, Orji AU, Orpana HM, Ortiz A, Ostroff SM, Otstavnov N, Otstavnov SS, Øverland S, Owolabi MO, P A M, Padubidri JR, Pakhare AP, Palladino R, Pana A, Panda-Jonas S, Pandey A, Park EK, Parmar PGK, Pasupula DK, Patel SK, Paternina-Caicedo AJ, Pathak A, Pathak M, Patten SB, Patton GC, Paudel D, Pazoki Toroudi H, Peden AE, Pennini A, Pepito VCF, Peprah EK, Pereira A, Pereira DM, Perico N, Pham HQ, Phillips MR, Pigott DM, Pilgrim T, Pilz TM, Pirsaheb M, Plana-Ripoll O, Plass D, Pokhrel KN, Polibin RV, Polinder S, Polkinghorne KR, Postma MJ, Pourjafar H, Pourmalek F, Pourmirza Kalhori R, Pourshams A, Poznańska A, Prada SI, Prakash V, Pribadi DRA, Pupillo E, Quazi Syed Z, Rabiee M, Rabiee N, Radfar A, Rafiee A, Rafiei A, Raggi A, Rahimi-Movaghar A, Rahman MA, Rajabpour-Sanati A, Rajati F, Ramezanzadeh K, Ranabhat CL, Rao PC, Rao SJ, Rasella D, Rastogi P, Rathi P, Rawaf DL, Rawaf S, Rawal L, Razo C, Redford SB, Reiner RC, Reinig N, Reitsma MB, Remuzzi G, Renjith V, Renzaho AMN, Resnikoff S, Rezaei N, Rezai MS, Rezapour A, Rhinehart PA, Riahi SM, Ribeiro ALP, Ribeiro DC, Ribeiro D, Rickard J, Roberts NLS, Roberts S, Robinson SR, Roever L, Rolfe S, Ronfani L, Roshandel G, Roth GA, Rubagotti E, Rumisha SF, Sabour S, Sachdev PS, Saddik B, Sadeghi E, Sadeghi M, Saeidi S, Safi S, Safiri S, Sagar R, Sahebkar A, Sahraian MA, Sajadi SM, Salahshoor MR, Salamati P, Salehi Zahabi S, Salem H, Salem MRR, Salimzadeh H, Salomon JA, Salz I, Samad Z, Samy AM, Sanabria J, Santomauro DF, Santos IS, Santos JV, Santric-Milicevic MM, Saraswathy SYI, Sarmiento-Suárez R, Sarrafzadegan N, Sartorius B, Sarveazad A, Sathian B, Sathish T, Sattin D, Sbarra AN, Schaeffer LE, Schiavolin S, Schmidt MI, Schutte AE, Schwebel DC, Schwendicke F, Senbeta AM, Senthilkumaran S, Sepanlou SG, Shackelford KA, Shadid J, Shahabi S, Shaheen AA, Shaikh MA, Shalash AS, Shams-Beyranvand M, Shamsizadeh M, Shannawaz M, Sharafi K, Sharara F, Sheena BS, Sheikhtaheri A, Shetty RS, Shibuya K, Shiferaw WS, Shigematsu M, Shin JI, Shiri R, Shirkoohi R, Shrime MG, Shuval K, Siabani S, Sigfusdottir ID, Sigurvinsdottir R, Silva JP, Simpson KE, Singh A, Singh JA, Skiadaresi E, Skou ST, Skryabin VY, Sobngwi E, Sokhan A, Soltani S, Sorensen RJD, Soriano JB, Sorrie MB, Soyiri IN, Sreeramareddy CT, Stanaway JD, Stark BA, Ştefan SC, Stein C, Steiner C, Steiner TJ, Stokes MA, Stovner LJ, Stubbs JL, Sudaryanto A, Sufiyan MB, Sulo G, Sultan I, Sykes BL, Sylte DO, Szócska M, Tabarés-Seisdedos R, Tabb KM, Tadakamadla SK, Taherkhani A, Tajdini M, Takahashi K, Taveira N, Teagle WL, Teame H, Tehrani-Banihashemi A, Teklehaimanot BF, Terrason S, Tessema ZT, Thankappan KR, Thomson AM, Tohidinik HR, Tonelli M, Topor-Madry R, Torre AE, Touvier M, Tovani-Palone MRR, Tran BX, Travillian R, Troeger CE, Truelsen TC, Tsai AC, Tsatsakis A, Tudor Car L, Tyrovolas S, Uddin R, Ullah S, Undurraga EA, Unnikrishnan B, Vacante M, Vakilian A, Valdez PR, Varughese S, Vasankari TJ, Vasseghian Y, Venketasubramanian N, Violante FS, Vlassov V, Vollset SE, Vongpradith A, Vukovic A, Vukovic R, Waheed Y, Walters MK, Wang J, Wang Y, Wang YP, Ward JL, Watson A, Wei J, Weintraub RG, Weiss DJ, Weiss J, Westerman R, Whisnant JL, Whiteford HA, Wiangkham T, Wiens KE, Wijeratne T, Wilner LB, Wilson S, Wojtyniak B, Wolfe CDA, Wool EE, Wu AM, Wulf Hanson S, Wunrow HY, Xu G, Xu R, Yadgir S, Yahyazadeh Jabbari SH, Yamagishi K, Yaminfirooz M, Yano Y, Yaya S, Yazdi-Feyzabadi V, Yearwood JA, Yeheyis TY, Yeshitila YG, Yip P, Yonemoto N, Yoon SJ, Yoosefi Lebni J, Younis MZ, Younker TP, Yousefi Z, Yousefifard M, Yousefinezhadi T, Yousuf AY, Yu C, Yusefzadeh H, Zahirian Moghadam T, Zaki L, Zaman SB, Zamani M, Zamanian M, Zandian H, Zangeneh A, Zastrozhin MS, Zewdie KA, Zhang Y, Zhang ZJ, Zhao JT, Zhao Y, Zheng P, Zhou M, Ziapour A, Zimsen SRM, Naghavi M, Murray CJL. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 2020; 396:1204-1222. [PMID: 33069326 PMCID: PMC7567026 DOI: 10.1016/s0140-6736(20)30925-9] [Citation(s) in RCA: 6299] [Impact Index Per Article: 1574.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2019] [Revised: 02/27/2020] [Accepted: 04/14/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. METHODS GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. FINDINGS Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990-2010 time period, with the greatest annualised rate of decline occurring in the 0-9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10-24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10-24 years were also in the top ten in the 25-49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50-74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. INTERPRETATION As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and development investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. FUNDING Bill & Melinda Gates Foundation.
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Murray CJL, Aravkin AY, Zheng P, Abbafati C, Abbas KM, Abbasi-Kangevari M, Abd-Allah F, Abdelalim A, Abdollahi M, Abdollahpour I, Abegaz KH, Abolhassani H, Aboyans V, Abreu LG, Abrigo MRM, Abualhasan A, Abu-Raddad LJ, Abushouk AI, Adabi M, Adekanmbi V, Adeoye AM, Adetokunboh OO, Adham D, Advani SM, Agarwal G, Aghamir SMK, Agrawal A, Ahmad T, Ahmadi K, Ahmadi M, Ahmadieh H, Ahmed MB, Akalu TY, Akinyemi RO, Akinyemiju T, Akombi B, Akunna CJ, Alahdab F, Al-Aly Z, Alam K, Alam S, Alam T, Alanezi FM, Alanzi TM, Alemu BW, Alhabib KF, Ali M, Ali S, Alicandro G, Alinia C, Alipour V, Alizade H, Aljunid SM, Alla F, Allebeck P, Almasi-Hashiani A, Al-Mekhlafi HM, Alonso J, Altirkawi KA, Amini-Rarani M, Amiri F, Amugsi DA, Ancuceanu R, Anderlini D, Anderson JA, Andrei CL, Andrei T, Angus C, Anjomshoa M, Ansari F, Ansari-Moghaddam A, Antonazzo IC, Antonio CAT, Antony CM, Antriyandarti E, Anvari D, Anwer R, Appiah SCY, Arabloo J, Arab-Zozani M, Ariani F, Armoon B, Ärnlöv J, Arzani A, Asadi-Aliabadi M, Asadi-Pooya AA, Ashbaugh C, Assmus M, Atafar Z, Atnafu DD, Atout MMW, Ausloos F, Ausloos M, Ayala Quintanilla BP, Ayano G, Ayanore MA, Azari S, Azarian G, Azene ZN, Badawi A, Badiye AD, Bahrami MA, Bakhshaei MH, Bakhtiari A, Bakkannavar SM, Baldasseroni A, Ball K, Ballew SH, Balzi D, Banach M, Banerjee SK, Bante AB, Baraki AG, Barker-Collo SL, Bärnighausen TW, Barrero LH, Barthelemy CM, Barua L, Basu S, Baune BT, Bayati M, Becker JS, Bedi N, Beghi E, Béjot Y, Bell ML, Bennitt FB, Bensenor IM, Berhe K, Berman AE, Bhagavathula AS, Bhageerathy R, Bhala N, Bhandari D, Bhattacharyya K, Bhutta ZA, Bijani A, Bikbov B, Bin Sayeed MS, Biondi A, Birihane BM, Bisignano C, Biswas RK, Bitew H, Bohlouli S, Bohluli M, Boon-Dooley AS, Borges G, Borzì AM, Borzouei S, Bosetti C, Boufous S, Braithwaite D, Breitborde NJK, Breitner S, Brenner H, Briant PS, Briko AN, Briko NI, Britton GB, Bryazka D, Bumgarner BR, Burkart K, Burnett RT, Burugina Nagaraja S, Butt ZA, Caetano dos Santos FL, Cahill LE, Cámera LLAA, Campos-Nonato IR, Cárdenas R, Carreras G, Carrero JJ, Carvalho F, Castaldelli-Maia JM, Castañeda-Orjuela CA, Castelpietra G, Castro F, Causey K, Cederroth CR, Cercy KM, Cerin E, Chandan JS, Chang KL, Charlson FJ, Chattu VK, Chaturvedi S, Cherbuin N, Chimed-Ochir O, Cho DY, Choi JYJ, Christensen H, Chu DT, Chung MT, Chung SC, Cicuttini FM, Ciobanu LG, Cirillo M, Classen TKD, Cohen AJ, Compton K, Cooper OR, Costa VM, Cousin E, Cowden RG, Cross DH, Cruz JA, Dahlawi SMA, Damasceno AAM, Damiani G, Dandona L, Dandona R, Dangel WJ, Danielsson AK, Dargan PI, Darwesh AM, Daryani A, Das JK, Das Gupta R, das Neves J, Dávila-Cervantes CA, Davitoiu DV, De Leo D, Degenhardt L, DeLang M, Dellavalle RP, Demeke FM, Demoz GT, Demsie DG, Denova-Gutiérrez E, Dervenis N, Dhungana GP, Dianatinasab M, Dias da Silva D, Diaz D, Dibaji Forooshani ZS, Djalalinia S, Do HT, Dokova K, Dorostkar F, Doshmangir L, Driscoll TR, Duncan BB, Duraes AR, Eagan AW, Edvardsson D, El Nahas N, El Sayed I, El Tantawi M, Elbarazi I, Elgendy IY, El-Jaafary SI, Elyazar IRF, Emmons-Bell S, Erskine HE, Eskandarieh S, Esmaeilnejad S, Esteghamati A, Estep K, Etemadi A, Etisso AE, Fanzo J, Farahmand M, Fareed M, Faridnia R, Farioli A, Faro A, Faruque M, Farzadfar F, Fattahi N, Fazlzadeh M, Feigin VL, Feldman R, Fereshtehnejad SM, Fernandes E, Ferrara G, Ferrari AJ, Ferreira ML, Filip I, Fischer F, Fisher JL, Flor LS, Foigt NA, Folayan MO, Fomenkov AA, Force LM, Foroutan M, Franklin RC, Freitas M, Fu W, Fukumoto T, Furtado JM, Gad MM, Gakidou E, Gallus S, Garcia-Basteiro AL, Gardner WM, Geberemariyam BS, Gebreslassie AAAA, Geremew A, Gershberg Hayoon A, Gething PW, Ghadimi M, Ghadiri K, Ghaffarifar F, Ghafourifard M, Ghamari F, Ghashghaee A, Ghiasvand H, Ghith N, Gholamian A, Ghosh R, Gill PS, Ginindza TGG, Giussani G, Gnedovskaya EV, Goharinezhad S, Gopalani SV, Gorini G, Goudarzi H, Goulart AC, Greaves F, Grivna M, Grosso G, Gubari MIM, Gugnani HC, Guimarães RA, Guled RA, Guo G, Guo Y, Gupta R, 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Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 2020; 396:1223-1249. [PMID: 33069327 PMCID: PMC7566194 DOI: 10.1016/s0140-6736(20)30752-2] [Citation(s) in RCA: 3234] [Impact Index Per Article: 808.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 03/21/2020] [Accepted: 03/23/2020] [Indexed: 02/07/2023]
Abstract
BACKGROUND Rigorous analysis of levels and trends in exposure to leading risk factors and quantification of their effect on human health are important to identify where public health is making progress and in which cases current efforts are inadequate. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a standardised and comprehensive assessment of the magnitude of risk factor exposure, relative risk, and attributable burden of disease. METHODS GBD 2019 estimated attributable mortality, years of life lost (YLLs), years of life lived with disability (YLDs), and disability-adjusted life-years (DALYs) for 87 risk factors and combinations of risk factors, at the global level, regionally, and for 204 countries and territories. GBD uses a hierarchical list of risk factors so that specific risk factors (eg, sodium intake), and related aggregates (eg, diet quality), are both evaluated. This method has six analytical steps. (1) We included 560 risk-outcome pairs that met criteria for convincing or probable evidence on the basis of research studies. 12 risk-outcome pairs included in GBD 2017 no longer met inclusion criteria and 47 risk-outcome pairs for risks already included in GBD 2017 were added based on new evidence. (2) Relative risks were estimated as a function of exposure based on published systematic reviews, 81 systematic reviews done for GBD 2019, and meta-regression. (3) Levels of exposure in each age-sex-location-year included in the study were estimated based on all available data sources using spatiotemporal Gaussian process regression, DisMod-MR 2.1, a Bayesian meta-regression method, or alternative methods. (4) We determined, from published trials or cohort studies, the level of exposure associated with minimum risk, called the theoretical minimum risk exposure level. (5) Attributable deaths, YLLs, YLDs, and DALYs were computed by multiplying population attributable fractions (PAFs) by the relevant outcome quantity for each age-sex-location-year. (6) PAFs and attributable burden for combinations of risk factors were estimated taking into account mediation of different risk factors through other risk factors. Across all six analytical steps, 30 652 distinct data sources were used in the analysis. Uncertainty in each step of the analysis was propagated into the final estimates of attributable burden. Exposure levels for dichotomous, polytomous, and continuous risk factors were summarised with use of the summary exposure value to facilitate comparisons over time, across location, and across risks. Because the entire time series from 1990 to 2019 has been re-estimated with use of consistent data and methods, these results supersede previously published GBD estimates of attributable burden. FINDINGS The largest declines in risk exposure from 2010 to 2019 were among a set of risks that are strongly linked to social and economic development, including household air pollution; unsafe water, sanitation, and handwashing; and child growth failure. Global declines also occurred for tobacco smoking and lead exposure. The largest increases in risk exposure were for ambient particulate matter pollution, drug use, high fasting plasma glucose, and high body-mass index. In 2019, the leading Level 2 risk factor globally for attributable deaths was high systolic blood pressure, which accounted for 10·8 million (95% uncertainty interval [UI] 9·51-12·1) deaths (19·2% [16·9-21·3] of all deaths in 2019), followed by tobacco (smoked, second-hand, and chewing), which accounted for 8·71 million (8·12-9·31) deaths (15·4% [14·6-16·2] of all deaths in 2019). The leading Level 2 risk factor for attributable DALYs globally in 2019 was child and maternal malnutrition, which largely affects health in the youngest age groups and accounted for 295 million (253-350) DALYs (11·6% [10·3-13·1] of all global DALYs that year). The risk factor burden varied considerably in 2019 between age groups and locations. Among children aged 0-9 years, the three leading detailed risk factors for attributable DALYs were all related to malnutrition. Iron deficiency was the leading risk factor for those aged 10-24 years, alcohol use for those aged 25-49 years, and high systolic blood pressure for those aged 50-74 years and 75 years and older. INTERPRETATION Overall, the record for reducing exposure to harmful risks over the past three decades is poor. Success with reducing smoking and lead exposure through regulatory policy might point the way for a stronger role for public policy on other risks in addition to continued efforts to provide information on risk factor harm to the general public. FUNDING Bill & Melinda Gates Foundation.
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Mohammed H, Mohammed S, Mohebi F, Mohseni Bandpei MA, Mokari A, Mokdad AH, Momen NC, Monasta L, Mooney MD, Moradi G, Moradi M, Moradi-Joo M, Moradi-Lakeh M, Moradzadeh R, Moraga P, Moreno Velásquez I, Morgado-da-Costa J, Morrison SD, Mosser JF, Mouodi S, Mousavi SM, Mousavi Khaneghah A, Mueller UO, Musa KI, Muthupandian S, Nabavizadeh B, Naderi M, Nagarajan AJ, Naghavi M, Naghshtabrizi B, Naik G, Najafi F, Nangia V, Nansseu JR, Ndwandwe DE, Negoi I, Negoi RI, Ngunjiri JW, Nguyen HLT, Nguyen TH, Nigatu YT, Nikbakhsh R, Nikpoor AR, Nixon MR, Nnaji CA, Nomura S, Noubiap JJ, Nouraei Motlagh S, Nowak C, Oţoiu A, Odell CM, Oh IH, Oladnabi M, Olagunju AT, Olusanya BO, Olusanya JO, Omar Bali A, Ong KL, Onwujekwe OE, Ortiz A, Otstavnov N, Otstavnov SS, Øverland S, Owolabi MO, P A M, Padubidri JR, Pakshir K, Palladino R, Pana A, Panda-Jonas S, Park J, Pasupula DK, Patel JR, Patel SK, Patton GC, Paulson KR, Pazoki Toroudi H, Pease SA, Peden AE, Pepito VCF, Peprah EK, Pereira A, Pereira DM, Perico N, Pigott DM, Pilgrim T, Pilz TM, Piradov MA, Pirsaheb M, Pokhrel KN, Postma MJ, Pourjafar H, Pourmalek F, Pourshams A, Poznańska A, Prada SI, Prakash S, Preotescu L, Quazi Syed Z, Rabiee M, Rabiee N, Radfar A, Rafiei A, Raggi A, Rahman MA, Rajabpour-Sanati A, Ram P, Ranabhat CL, Rao SJ, Rasella D, Rashedi V, Rastogi P, Rathi P, Rawal L, Remuzzi G, Renjith V, Renzaho AMN, Resnikoff S, Rezaei N, Rezai MS, Rezapour A, Rickard J, Roever L, Ronfani L, Roshandel G, Rostamian M, Rubagotti E, Rwegerera GM, Sabour S, Saddik B, Sadeghi E, Sadeghi M, Saeedi Moghaddam S, Safari Y, Safi S, Safiri S, Sagar R, Sahebkar A, Sahraian MA, Sajadi SM, Salahshoor MR, Salama JS, Salamati P, Salem MRR, Salimi Y, Salomon JA, Salz I, Samad Z, Samy AM, Sanabria J, Santric-Milicevic MM, Saraswathy SYI, Sartorius B, Sarveazad A, Sathian B, Sathish T, Sattin D, Saylan M, Schaeffer LE, Schiavolin S, Schwebel DC, Schwendicke F, Sekerija M, Senbeta AM, Senthilkumaran S, Sepanlou SG, Serván-Mori E, Shabani M, Shahabi S, Shahbaz M, Shaheen AA, Shaikh MA, Shalash AS, Shams-Beyranvand M, Shamsi M, Shamsizadeh M, Shannawaz M, Sharafi K, Sharafi Z, Sharara F, Sharma R, Shaw DH, Sheikh A, Shin JI, Shiri R, Shrime MG, Shuval K, Siabani S, Sigfusdottir ID, Sigurvinsdottir R, Silva DAS, Simonetti B, Simpson KE, Singh JA, Skiadaresi E, Skryabin VY, Soheili A, Sokhan A, Sorensen RJD, Soriano JB, Sorrie MB, Soyiri IN, Spurlock EE, Sreeramareddy CT, Stockfelt L, Stokes MA, Stubbs JL, Sudaryanto A, Sufiyan MB, Suliankatchi Abdulkader R, Sykes BL, Tabarés-Seisdedos R, Tabb KM, Tadakamadla SK, Taherkhani A, Tang M, Taveira N, Taylor HJ, Teagle WL, Tehrani-Banihashemi A, Teklehaimanot BF, Tessema ZT, Thankappan KR, Thomas N, Thrift AG, Titova MV, Tohidinik HR, Tonelli M, Topor-Madry R, Topouzis F, Tovani-Palone MRR, Traini E, Tran BX, Travillian R, Trias-Llimós S, Truelsen TC, Tudor Car L, Unnikrishnan B, Upadhyay E, Vacante M, Vakilian A, Valdez PR, Valli A, Vardavas C, Vasankari TJ, Vasconcelos AMN, Vasseghian Y, Veisani Y, Venketasubramanian N, Vidale S, Violante FS, Vlassov V, Vollset SE, Vos T, Vujcic IS, Vukovic A, Vukovic R, Waheed Y, Wallin MT, Walters MK, Wang H, Wang YP, Watson S, Wei J, Weiss J, Weldesamuel GT, Werdecker A, Westerman R, Whiteford HA, Wiangkham T, Wiens KE, Wijeratne T, Wiysonge CS, Wojtyniak B, Wolfe CDA, Wondmieneh AB, Wool EE, Wu AM, Wu J, Xu G, Yamada T, Yamagishi K, Yano Y, Yaya S, Yazdi-Feyzabadi V, Yearwood JA, Yeheyis TY, Yilgwan CS, Yip P, Yonemoto N, Yoon SJ, Yoosefi Lebni J, York HW, Younis MZ, Younker TP, Yousefi Z, Yousefinezhadi T, Yousuf AY, Yusefzadeh H, Zahirian Moghadam T, Zakzuk J, Zaman SB, Zamani M, Zamanian M, Zandian H, Zhang ZJ, Zheng P, Zhou M, Ziapour A, Murray CJL. Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950-2019: a comprehensive demographic analysis for the Global Burden of Disease Study 2019. Lancet 2020; 396:1160-1203. [PMID: 33069325 PMCID: PMC7566045 DOI: 10.1016/s0140-6736(20)30977-6] [Citation(s) in RCA: 701] [Impact Index Per Article: 175.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2019] [Revised: 03/10/2020] [Accepted: 04/20/2020] [Indexed: 02/07/2023]
Abstract
BACKGROUND Accurate and up-to-date assessment of demographic metrics is crucial for understanding a wide range of social, economic, and public health issues that affect populations worldwide. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 produced updated and comprehensive demographic assessments of the key indicators of fertility, mortality, migration, and population for 204 countries and territories and selected subnational locations from 1950 to 2019. METHODS 8078 country-years of vital registration and sample registration data, 938 surveys, 349 censuses, and 238 other sources were identified and used to estimate age-specific fertility. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate age-specific fertility rates for 5-year age groups between ages 15 and 49 years. With extensions to age groups 10-14 and 50-54 years, the total fertility rate (TFR) was then aggregated using the estimated age-specific fertility between ages 10 and 54 years. 7417 sources were used for under-5 mortality estimation and 7355 for adult mortality. ST-GPR was used to synthesise data sources after correction for known biases. Adult mortality was measured as the probability of death between ages 15 and 60 years based on vital registration, sample registration, and sibling histories, and was also estimated using ST-GPR. HIV-free life tables were then estimated using estimates of under-5 and adult mortality rates using a relational model life table system created for GBD, which closely tracks observed age-specific mortality rates from complete vital registration when available. Independent estimates of HIV-specific mortality generated by an epidemiological analysis of HIV prevalence surveys and antenatal clinic serosurveillance and other sources were incorporated into the estimates in countries with large epidemics. Annual and single-year age estimates of net migration and population for each country and territory were generated using a Bayesian hierarchical cohort component model that analysed estimated age-specific fertility and mortality rates along with 1250 censuses and 747 population registry years. We classified location-years into seven categories on the basis of the natural rate of increase in population (calculated by subtracting the crude death rate from the crude birth rate) and the net migration rate. We computed healthy life expectancy (HALE) using years lived with disability (YLDs) per capita, life tables, and standard demographic methods. Uncertainty was propagated throughout the demographic estimation process, including fertility, mortality, and population, with 1000 draw-level estimates produced for each metric. FINDINGS The global TFR decreased from 2·72 (95% uncertainty interval [UI] 2·66-2·79) in 2000 to 2·31 (2·17-2·46) in 2019. Global annual livebirths increased from 134·5 million (131·5-137·8) in 2000 to a peak of 139·6 million (133·0-146·9) in 2016. Global livebirths then declined to 135·3 million (127·2-144·1) in 2019. Of the 204 countries and territories included in this study, in 2019, 102 had a TFR lower than 2·1, which is considered a good approximation of replacement-level fertility. All countries in sub-Saharan Africa had TFRs above replacement level in 2019 and accounted for 27·1% (95% UI 26·4-27·8) of global livebirths. Global life expectancy at birth increased from 67·2 years (95% UI 66·8-67·6) in 2000 to 73·5 years (72·8-74·3) in 2019. The total number of deaths increased from 50·7 million (49·5-51·9) in 2000 to 56·5 million (53·7-59·2) in 2019. Under-5 deaths declined from 9·6 million (9·1-10·3) in 2000 to 5·0 million (4·3-6·0) in 2019. Global population increased by 25·7%, from 6·2 billion (6·0-6·3) in 2000 to 7·7 billion (7·5-8·0) in 2019. In 2019, 34 countries had negative natural rates of increase; in 17 of these, the population declined because immigration was not sufficient to counteract the negative rate of decline. Globally, HALE increased from 58·6 years (56·1-60·8) in 2000 to 63·5 years (60·8-66·1) in 2019. HALE increased in 202 of 204 countries and territories between 2000 and 2019. INTERPRETATION Over the past 20 years, fertility rates have been dropping steadily and life expectancy has been increasing, with few exceptions. Much of this change follows historical patterns linking social and economic determinants, such as those captured by the GBD Socio-demographic Index, with demographic outcomes. More recently, several countries have experienced a combination of low fertility and stagnating improvement in mortality rates, pushing more populations into the late stages of the demographic transition. Tracking demographic change and the emergence of new patterns will be essential for global health monitoring. FUNDING Bill & Melinda Gates Foundation.
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Lucas TC, Nandi AK, Keddie SH, Chestnutt EG, Howes RE, Rumisha SF, Arambepola R, Bertozzi-Villa A, Python A, Symons TL, Millar JJ, Amratia P, Hancock P, Battle KE, Cameron E, Gething PW, Weiss DJ. Improving disaggregation models of malaria incidence by ensembling non-linear models of prevalence. Spat Spatiotemporal Epidemiol 2020; 41:100357. [PMID: 35691633 PMCID: PMC9205339 DOI: 10.1016/j.sste.2020.100357] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 04/13/2020] [Accepted: 06/18/2020] [Indexed: 10/24/2022]
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Hancock PA, Hendriks CJM, Tangena JA, Gibson H, Hemingway J, Coleman M, Gething PW, Cameron E, Bhatt S, Moyes CL. Mapping trends in insecticide resistance phenotypes in African malaria vectors. PLoS Biol 2020; 18:e3000633. [PMID: 32584814 PMCID: PMC7316233 DOI: 10.1371/journal.pbio.3000633] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 05/11/2020] [Indexed: 11/25/2022] Open
Abstract
Mitigating the threat of insecticide resistance in African malaria vector populations requires comprehensive information about where resistance occurs, to what degree, and how this has changed over time. Estimating these trends is complicated by the sparse, heterogeneous distribution of observations of resistance phenotypes in field populations. We use 6,423 observations of the prevalence of resistance to the most important vector control insecticides to inform a Bayesian geostatistical ensemble modelling approach, generating fine-scale predictive maps of resistance phenotypes in mosquitoes from the Anopheles gambiae complex across Africa. Our models are informed by a suite of 111 predictor variables describing potential drivers of selection for resistance. Our maps show alarming increases in the prevalence of resistance to pyrethroids and DDT across sub-Saharan Africa from 2005 to 2017, with mean mortality following insecticide exposure declining from almost 100% to less than 30% in some areas, as well as substantial spatial variation in resistance trends.
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Affiliation(s)
| | | | - Julie-Anne Tangena
- Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, United Kingdom
| | - Harry Gibson
- Big Data Institute, University of Oxford, Oxford, United Kingdom
| | - Janet Hemingway
- Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, United Kingdom
| | - Michael Coleman
- Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, United Kingdom
| | - Peter W. Gething
- Telethon Kids Institute, Perth Children's Hospital, Perth, Australia
- Curtin University, Bentley, Perth, Australia
| | - Ewan Cameron
- Big Data Institute, University of Oxford, Oxford, United Kingdom
| | - Samir Bhatt
- Department of Infectious Disease Epidemiology, Imperial College, St Mary’s Hospital, London, United Kingdom
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Tusting LS, Gething PW, Gibson HS, Greenwood B, Knudsen J, Lindsay SW, Bhatt S. Housing and child health in sub-Saharan Africa: A cross-sectional analysis. PLoS Med 2020; 17:e1003055. [PMID: 32203504 PMCID: PMC7089421 DOI: 10.1371/journal.pmed.1003055] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 02/19/2020] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Housing is essential to human well-being but neglected in global health. Today, housing in Africa is rapidly improving alongside economic development, creating an urgent need to understand how these changes can benefit health. We hypothesised that improved housing is associated with better health in children living in sub-Saharan Africa (SSA). We conducted a cross-sectional analysis of housing conditions relative to a range of child health outcomes in SSA. METHODS AND FINDINGS Cross-sectional data were analysed for 824,694 children surveyed in 54 Demographic and Health Surveys, 21 Malaria Indicator Surveys, and two AIDS Indicator Surveys conducted in 33 countries between 2001 and 2017 that measured malaria infection by microscopy or rapid diagnostic test (RDT), diarrhoea, acute respiratory infections (ARIs), stunting, wasting, underweight, or anaemia in children aged 0-5 years. The mean age of children was 2.5 years, and 49.7% were female. Housing was categorised into a binary variable based on a United Nations definition comparing improved housing (with improved drinking water, improved sanitation, sufficient living area, and finished building materials) versus unimproved housing (all other houses). Associations between house type and child health outcomes were determined using conditional logistic regression within surveys, adjusting for prespecified covariables including age, sex, household wealth, insecticide-treated bed net use, and vaccination status. Individual survey odds ratios (ORs) were pooled using random-effects meta-analysis. Across surveys, improved housing was associated with 8%-18% lower odds of all outcomes except ARI (malaria infection by microscopy: adjusted OR [aOR] 0.88, 95% confidence intervals [CIs] 0.80-0.97, p = 0.01; malaria infection by RDT: aOR 0.82, 95% CI 0.77-0.88, p < 0.001; diarrhoea: aOR 0.92, 95% CI 0.88-0.97, p = 0.001; ARI: aOR 0.96, 95% CI 0.87-1.07, p = 0.49; stunting: aOR 0.83, 95% CI 0.77-0.88, p < 0.001; wasting: aOR 0.90, 95% CI 0.83-0.99, p = 0.03; underweight: aOR 0.85, 95% CI 0.80-0.90, p < 0.001; any anaemia: aOR 0.87, 95% CI 0.82-0.92, p < 0.001; severe anaemia: aOR 0.89, 95% CI 0.84-0.95, p < 0.001). In comparison, insecticide-treated net use was associated with 16%-17% lower odds of malaria infection (microscopy: aOR 0.83, 95% CI 0.78-0.88, p < 0.001; RDT: aOR 0.84, 95% CI 0.79-0.88, p < 0.001). Drinking water source and sanitation facility alone were not associated with diarrhoea. The main study limitations are the use of self-reported diarrhoea and ARI, as well as potential residual confounding by socioeconomic position, despite adjustments for household wealth and education. CONCLUSIONS In this study, we observed that poor housing, which includes inadequate drinking water and sanitation facility, is associated with health outcomes known to increase child mortality in SSA. Improvements to housing may be protective against a number of important childhood infectious diseases as well as poor growth outcomes, with major potential to improve children's health and survival across SSA.
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Affiliation(s)
- Lucy S. Tusting
- Department of Disease Control, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Peter W. Gething
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, United Kingdom
| | - Harry S. Gibson
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, United Kingdom
| | - Brian Greenwood
- Department of Disease Control, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Jakob Knudsen
- The Royal Danish Academy of Fine Arts, Schools of Architecture, Design and Conservation, Copenhagen, Denmark
| | - Steve W. Lindsay
- Department of Biosciences, Durham University, Durham, United Kingdom
| | - Samir Bhatt
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, United Kingdom
- Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
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Nguyen M, Howes RE, Lucas TCD, Battle KE, Cameron E, Gibson HS, Rozier J, Keddie S, Collins E, Arambepola R, Kang SY, Hendriks C, Nandi A, Rumisha SF, Bhatt S, Mioramalala SA, Nambinisoa MA, Rakotomanana F, Gething PW, Weiss DJ. Mapping malaria seasonality in Madagascar using health facility data. BMC Med 2020; 18:26. [PMID: 32036785 PMCID: PMC7008536 DOI: 10.1186/s12916-019-1486-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 12/20/2019] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Many malaria-endemic areas experience seasonal fluctuations in case incidence as Anopheles mosquito and Plasmodium parasite life cycles respond to changing environmental conditions. Identifying location-specific seasonality characteristics is useful for planning interventions. While most existing maps of malaria seasonality use fixed thresholds of rainfall, temperature, and/or vegetation indices to identify suitable transmission months, we construct a statistical modelling framework for characterising the seasonal patterns derived directly from monthly health facility data. METHODS With data from 2669 of the 3247 health facilities in Madagascar, a spatiotemporal regression model was used to estimate seasonal patterns across the island. In the absence of catchment population estimates or the ability to aggregate to the district level, this focused on the monthly proportions of total annual cases by health facility level. The model was informed by dynamic environmental covariates known to directly influence seasonal malaria trends. To identify operationally relevant characteristics such as the transmission start months and associated uncertainty measures, an algorithm was developed and applied to model realisations. A seasonality index was used to incorporate burden information from household prevalence surveys and summarise 'how seasonal' locations are relative to their surroundings. RESULTS Positive associations were detected between monthly case proportions and temporally lagged covariates of rainfall and temperature suitability. Consistent with the existing literature, model estimates indicate that while most parts of Madagascar experience peaks in malaria transmission near March-April, the eastern coast experiences an earlier peak around February. Transmission was estimated to start in southeast districts before southwest districts, suggesting that indoor residual spraying should be completed in the same order. In regions where the data suggested conflicting seasonal signals or two transmission seasons, estimates of seasonal features had larger deviations and therefore less certainty. CONCLUSIONS Monthly health facility data can be used to establish seasonal patterns in malaria burden and augment the information provided by household prevalence surveys. The proposed modelling framework allows for evidence-based and cohesive inferences on location-specific seasonal characteristics. As health surveillance systems continue to improve, it is hoped that more of such data will be available to improve our understanding and planning of intervention strategies.
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Affiliation(s)
- Michele Nguyen
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
| | - Rosalind E Howes
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Tim C D Lucas
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Katherine E Battle
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Ewan Cameron
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Harry S Gibson
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Jennifer Rozier
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Suzanne Keddie
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Emma Collins
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Rohan Arambepola
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Su Yun Kang
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Chantal Hendriks
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Anita Nandi
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Susan F Rumisha
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Samir Bhatt
- Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | | | | | | | - Peter W Gething
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Daniel J Weiss
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
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25
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Howes RE, Hawa K, Andriamamonjy VF, Franchard T, Miarimbola R, Mioramalala SA, Rafamatanantsoa JF, Rahantamalala MAM, Rajaobary SH, Rajaonera HDG, Rakotonindrainy AP, Andrianjatonavalona CR, Randriamiarinjatovo DNAL, Randrianasolo FM, Razafindratovo RMR, Ravaoarimanga M, Ye M, Gething PW, Taylor CA. Update to: A stakeholder workshop about modelled maps of key malaria indicator survey indicators in Madagascar. Malar J 2020; 19:13. [PMID: 31918731 PMCID: PMC6953134 DOI: 10.1186/s12936-019-3052-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- Rosalind E Howes
- Malaria Atlas Project, Nuffield Department of Medicine, Big Data Institute, University of Oxford, Oxford, UK.
| | - Kaleem Hawa
- Malaria Atlas Project, Nuffield Department of Medicine, Big Data Institute, University of Oxford, Oxford, UK
| | | | - Thierry Franchard
- Ministry of Health, Antananarivo, Madagascar.,Faculty of Science, University of Antananarivo, Antananarivo, Madagascar
| | - Raharizo Miarimbola
- Ministry of Health, Antananarivo, Madagascar.,Department of Public Health, Faculty of Medicine, University of Antananarivo, Antananarivo, Madagascar
| | - Sedera Aurélien Mioramalala
- Ministry of Health, Antananarivo, Madagascar.,Department of Public Health, Faculty of Medicine, University of Antananarivo, Antananarivo, Madagascar
| | | | - Mirana Ando Mbolatiana Rahantamalala
- National Malaria Control Programme, Ministry of Health, Antananarivo, Madagascar.,Department of Public Health, Faculty of Medicine, University of Antananarivo, Antananarivo, Madagascar
| | | | | | | | | | | | | | | | | | - Maurice Ye
- MEASURE-Evaluation, ICF, Antananarivo, Madagascar
| | - Peter W Gething
- Malaria Atlas Project, Nuffield Department of Medicine, Big Data Institute, University of Oxford, Oxford, UK
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Burstein R, Henry NJ, Collison ML, Marczak LB, Sligar A, Watson S, Marquez N, Abbasalizad-Farhangi M, Abbasi M, Abd-Allah F, Abdoli A, Abdollahi M, Abdollahpour I, Abdulkader RS, Abrigo MRM, Acharya D, Adebayo OM, Adekanmbi V, Adham D, Afshari M, Aghaali M, Ahmadi K, Ahmadi M, Ahmadpour E, Ahmed R, Akal CG, Akinyemi JO, Alahdab F, Alam N, Alamene GM, Alene KA, Alijanzadeh M, Alinia C, Alipour V, Aljunid SM, Almalki MJ, Al-Mekhlafi HM, Altirkawi K, Alvis-Guzman N, Amegah AK, Amini S, Amit AML, Anbari Z, Androudi S, Anjomshoa M, Ansari F, Antonio CAT, Arabloo J, Arefi Z, Aremu O, Armoon B, Arora A, Artaman A, Asadi A, Asadi-Aliabadi M, Ashraf-Ganjouei A, Assadi R, Ataeinia B, Atre SR, Quintanilla BPA, Ayanore MA, Azari S, Babaee E, Babazadeh A, Badawi A, Bagheri S, Bagherzadeh M, Baheiraei N, Balouchi A, Barac A, Bassat Q, Baune BT, Bayati M, Bedi N, Beghi E, Behzadifar M, Behzadifar M, Belay YB, Bell B, Bell ML, Berbada DA, Bernstein RS, Bhattacharjee NV, Bhattarai S, Bhutta ZA, Bijani A, Bohlouli S, Breitborde NJK, Britton G, Browne AJ, Nagaraja SB, Busse R, Butt ZA, Car J, Cárdenas R, Castañeda-Orjuela CA, Cerin E, Chanie WF, Chatterjee P, Chu DT, Cooper C, Costa VM, Dalal K, Dandona L, Dandona R, Daoud F, Daryani A, Das Gupta R, Davis I, Davis Weaver N, Davitoiu DV, De Neve JW, Demeke FM, Demoz GT, Deribe K, Desai R, Deshpande A, Desyibelew HD, Dey S, Dharmaratne SD, Dhimal M, Diaz D, Doshmangir L, Duraes AR, Dwyer-Lindgren L, Earl L, Ebrahimi R, Ebrahimpour S, Effiong A, Eftekhari A, Ehsani-Chimeh E, El Sayed I, El Sayed Zaki M, El Tantawi M, El-Khatib Z, Emamian MH, Enany S, Eskandarieh S, Eyawo O, Ezalarab M, Faramarzi M, Fareed M, Faridnia R, Faro A, Fazaeli AA, Fazlzadeh M, Fentahun N, Fereshtehnejad SM, Fernandes JC, Filip I, Fischer F, Foigt NA, Foroutan M, Francis JM, Fukumoto T, Fullman N, Gallus S, Gebre DG, Gebrehiwot TT, Gebremeskel GG, Gessner BD, Geta B, Gething PW, Ghadimi R, Ghadiri K, Ghajarzadeh M, Ghashghaee A, Gill PS, Gill TK, Golding N, Gomes 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Mapping 123 million neonatal, infant and child deaths between 2000 and 2017. Nature 2019; 574:353-358. [PMID: 31619795 PMCID: PMC6800389 DOI: 10.1038/s41586-019-1545-0] [Citation(s) in RCA: 124] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Accepted: 08/06/2019] [Indexed: 11/23/2022]
Abstract
Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2-to end preventable child deaths by 2030-we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000-2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations.
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Affiliation(s)
- Roy Burstein
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Nathaniel J Henry
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Michael L Collison
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Laurie B Marczak
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Amber Sligar
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Stefanie Watson
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Neal Marquez
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | | | | | | | - Amir Abdoli
- Department of Parasitology and Mycology, Jahrom University of Medical Sciences, Jahrom, Iran
| | - Mohammad Abdollahi
- The Institute of Pharmaceutical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Ibrahim Abdollahpour
- Multiple Sclerosis Research Center, Tehran University of Medical Sciences, Tehran, Iran
- Department of Epidemiology, Arak University of Medical Sciences, Arak, Iran
| | | | - Michael R M Abrigo
- Research Department, Philippine Institute for Development Studies, Quezon City, The Philippines
| | - Dilaram Acharya
- Department of Preventive Medicine, Dongguk University, Gyeongju, South Korea
- Department of Community Medicine, Kathmandu University, Devdaha, Nepal
| | | | | | - Davoud Adham
- School of Health, Ardabil University of Medical Science, Ardabil, Iran
| | - Mahdi Afshari
- Department of Community Medicine, Zabol University of Medical Sciences, Zabol, Iran
| | - Mohammad Aghaali
- Department of Epidemiology and Biostatistics, Qom University of Medical Sciences, Qom, Iran
| | - Keivan Ahmadi
- School of Pharmacy, University of Lincoln, Lincoln, UK
| | - Mehdi Ahmadi
- Environmental Technologies Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Ehsan Ahmadpour
- Department of Parasitology and Mycology, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Rushdia Ahmed
- James P. Grant School of Public Health, Brac University, Dhaka, Bangladesh
- Health Systems and Population Studies Division, International Centre for Diarrhoeal Disease Research Bangladesh, Dhaka, Bangladesh
| | - Chalachew Genet Akal
- Department of Medical Laboratory Science, Bahir Dar University, Bahir Dar, Ethiopia
| | - Joshua O Akinyemi
- Epidemiology and Medical Statistics, University of Ibadan, Ibadan, Nigeria
| | - Fares Alahdab
- Evidence Based Practice Center, Mayo Clinic Foundation for Medical Education and Research, Rochester, MN, USA
| | - Noore Alam
- Prevention Division, Queensland Health, Herston, Queensland, Australia
| | | | - Kefyalew Addis Alene
- Institute of Public Health, University of Gondar, Gondar, Ethiopia
- Research School of Population Health, Australian National University, Canberra, Australian Capital Territory, Australia
| | | | - Cyrus Alinia
- Department of Health Care Management and Economics, Urmia University of Medical Science, Urmia, Iran
| | - Vahid Alipour
- Health Economics Department, Iran University of Medical Sciences, Tehran, Iran
- Health Management and Economics Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Syed Mohamed Aljunid
- Department of Health Policy and Management, Kuwait University, Safat, Kuwait
- International Centre for Casemix and Clinical Coding, National University of Malaysia, Bandar Tun Razak, Malaysia
| | - Mohammed J Almalki
- Faculty of Public Health and Tropical Medicine, Jazan University, Jazan, Saudi Arabia
- Jazan University, Jazan, Saudi Arabia
| | - Hesham M Al-Mekhlafi
- Medical Research Center, Jazan University, Jazan, Saudi Arabia
- Department of Medical Parasitology, Sana'a University, Sana'a, Yemen
| | | | - Nelson Alvis-Guzman
- Research Group in Health Economics, Universidad de Cartagena, Cartagena, Colombia
- Research Group in Hospital Management and Health Policies, Universidad de la Costa, Barranquilla, Colombia
| | | | - Saeed Amini
- Health Services Management Department, Arak University of Medical Sciences, Arak, Iran
| | - Arianna Maever Loreche Amit
- Department of Epidemiology and Biostatistics, University of the Philippines Manila, Manila, The Philippines
- Online Programs for Applied Learning, Johns Hopkins University, Baltimore, MD, USA
| | - Zohreh Anbari
- Health Services Management Department, Arak University of Medical Sciences, Arak, Iran
| | - Sofia Androudi
- Department of Medicine, University of Thessaly, Volos, Greece
| | - Mina Anjomshoa
- Social Determinants of Health Research Center, Rafsanjan University of Medical Sciences, Rafsanjan, Iran
| | - Fereshteh Ansari
- Research Center for Evidence Based Medicine-Health Management and Safety Promotion Research Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Carl Abelardo T Antonio
- Department of Health Policy and Administration, University of the Philippines Manila, Manila, The Philippines
- Department of Applied Social Sciences, Hong Kong Polytechnic University, Hong Kong, China
| | - Jalal Arabloo
- Health Management and Economics Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Zohreh Arefi
- Department of Health Promotion and Education, Tehran University of Medical Sciences, Tehran, Iran
| | - Olatunde Aremu
- School of Health Sciences, Birmingham City University, Birmingham, UK
| | - Bahram Armoon
- School of Nursing and Midwifery, Saveh University of Medical Sciences, Saveh, Iran
- Social Determinants of Health Research Center, Saveh University of Medical Sciences, Saveh, Iran
| | - Amit Arora
- School of Science and Health, Western Sydney University, Penrith, New South Wales, Australia
- Oral Health Services, Sydney Local Health District, Sydney, New South Wales, Australia
| | - Al Artaman
- Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Anvar Asadi
- Research Center for Environmental Determinants of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Mehran Asadi-Aliabadi
- Preventive Medicine and Public Health Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Amir Ashraf-Ganjouei
- Multiple Sclerosis Research Center, Tehran University of Medical Sciences, Tehran, Iran
- Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Reza Assadi
- Education Development Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Bahar Ataeinia
- Non-communicable Diseases Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Sachin R Atre
- Center for Clinical Global Health Education, Johns Hopkins University, Baltimore, MD, USA
- Dr D. Y. Patil Medical College, Pune, India
| | - Beatriz Paulina Ayala Quintanilla
- The Judith Lumley Centre, La Trobe University, Melbourne, Victoria, Australia
- General Office for Research and Technological Transfer, Peruvian National Institute of Health, Lima, Peru
| | - Martin Amogre Ayanore
- Department of Family and Community Health, University of Health and Allied Sciences, Ho, Ghana
| | - Samad Azari
- Health Management and Economics Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Ebrahim Babaee
- Preventive Medicine and Public Health Research Center, Iran University of Medical Sciences, Tehran, Iran
| | | | - Alaa Badawi
- Public Health Risk Sciences Division, Public Health Agency of Canada, Toronto, Ontario, Canada
- Department of Nutritional Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Soghra Bagheri
- Kermanshah University of Medical Sciences, Kermanshah, Iran
| | | | - Nafiseh Baheiraei
- Tissue Engineering and Applied Cell Sciences Division, Tarbiat Modares University, Tehran, Iran
- Division of Diseases, Advanced Technologies Research Group, Tehran, Iran
| | - Abbas Balouchi
- School of Nursing and Midwifery, Iran University of Medical Sciences, Tehran, Iran
| | - Aleksandra Barac
- Clinic for Infectious and Tropical Diseases, Clinical Center of Serbia, Belgrade, Serbia
- Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Quique Bassat
- Barcelona Institute for Global Health, University of Barcelona, Barcelona, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
| | - Bernhard T Baune
- Department of Psychiatry, Melbourne Medical School, Melbourne, Victoria, Australia
| | - Mohsen Bayati
- Health Human Resources Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Neeraj Bedi
- Jazan University, Jazan, Saudi Arabia
- Department of Community Medicine, Gandhi Medical College Bhopal, Bhopal, India
| | - Ettore Beghi
- Department of Neuroscience, Mario Negri Institute for Pharmacological Research, Milan, Italy
| | - Masoud Behzadifar
- Social Determinants of Health Research Center, Lorestan University of Medical Sciences, Khorramabad, Iran
| | - Meysam Behzadifar
- Hepatitis Research Center, Lorestan University of Medical Sciences, Khorramabad, Iran
| | - Yared Belete Belay
- Pharmacoepidemiology and Social Pharmacy, Mekelle University, Mekelle, Ethiopia
| | - Brent Bell
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Michelle L Bell
- School of Forestry and Environmental Studies, Yale University, New Haven, CT, USA
| | | | - Robert S Bernstein
- Hubert Department of Global Health, Emory University, Atlanta, GA, USA
- Department of Global Health, University of South Florida, Tampa, FL, USA
| | | | - Suraj Bhattarai
- London School of Hygiene & Tropical Medicine, London, UK
- Nepal Academy of Science & Technology, Patan, Nepal
| | - Zulfiqar A Bhutta
- The Centre for Global Child Health, Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada
- Center of Excellence in Women and Child Health, Aga Khan University, Karachi, Pakistan
| | - Ali Bijani
- Social Determinants of Health Research Center, Babol University of Medical Sciences, Babol, Iran
| | - Somayeh Bohlouli
- Department of Veterinary Medicine, Karaj Islamic Azad University, Kermanshah, Iran
| | - Nicholas J K Breitborde
- Department of Psychology, Ohio State University, Columbus, OH, USA
- Psychiatry and Behavioral Health Department, Ohio State University, Columbus, OH, USA
| | - Gabrielle Britton
- Neuroscience Department, Institute for Scientific Research and High Technology Services, City of Knowledge, Panama
- Gorgas Memorial Institute for Health Studies, Panama, Panama
| | - Annie J Browne
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | | | - Reinhard Busse
- Department for Health Care Management, Technical University of Berlin, Berlin, Germany
| | - Zahid A Butt
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
- Al Shifa School of Public Health, Al Shifa Trust Eye Hospital, Rawalpindi, Pakistan
| | - Josip Car
- Centre for Population Health Sciences, Nanyang Technological University, Singapore, Singapore
- Global Ehealth Unit, Imperial College London, London, UK
| | - Rosario Cárdenas
- Department of Population and Health, Metropolitan Autonomous University, Mexico City, Mexico
| | - Carlos A Castañeda-Orjuela
- Colombian National Health Observatory, National Institute of Health, Bogota, Colombia
- Epidemiology and Public Health Evaluation Group, National University of Colombia, Bogota, Colombia
| | - Ester Cerin
- Mary Mackillop Institute for Health Research, Australian Catholic University, Melbourne, Victoria, Australia
- School of Public Health, University of Hong Kong, Hong Kong, China
| | | | - Pranab Chatterjee
- Division of Epidemiology, National Institute of Cholera and Enteric Diseases, Kolkata, India
| | - Dinh-Toi Chu
- Faculty of Biology, Hanoi National University of Education, Hanoi, Vietnam
| | - Cyrus Cooper
- Department of Rheumatology, University of Oxford, Oxford, UK
- Medical Research Council Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - Vera M Costa
- Applied Molecular Biosciences Unit (UCIBIO), University of Porto, Porto, Portugal
| | - Koustuv Dalal
- Institute of Public Health Kalyani, Kalyani, India
- School of Health Science, Orebro University, Orebro, Sweden
| | - Lalit Dandona
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Public Health Foundation of India, Gurugram, India
| | - Rakhi Dandona
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Public Health Foundation of India, Gurugram, India
| | - Farah Daoud
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Ahmad Daryani
- Toxoplasmosis Research Center, Mazandaran University of Medical Sciences, Sari, Iran
| | - Rajat Das Gupta
- James P. Grant School of Public Health, Brac University, Dhaka, Bangladesh
| | - Ian Davis
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Nicole Davis Weaver
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Dragos Virgil Davitoiu
- Department of General Surgery, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
- Department of Surgery, Clinical Emergency Hospital St Pantelimon, Bucharest, Romania
| | - Jan-Walter De Neve
- Heidelberg Institute of Global Health (HIGH), Heidelberg University, Heidelberg, Germany
| | | | - Gebre Teklemariam Demoz
- School of Pharmacy, Aksum University, Aksum, Ethiopia
- Addis Ababa University, Addis Ababa, Ethiopia
| | - Kebede Deribe
- School of Public Health, Addis Ababa University, Addis Ababa, Ethiopia
- Department of Global Health and Infection, Brighton and Sussex Medical School, Brighton, UK
| | - Rupak Desai
- Division of Cardiology, Atlanta Veterans Affairs Medical Center, Decatur, GA, USA
| | - Aniruddha Deshpande
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | | | - Sagnik Dey
- Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, New Delhi, India
| | - Samath Dhamminda Dharmaratne
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Department of Community Medicine, University of Peradeniya, Peradeniya, Sri Lanka
| | - Meghnath Dhimal
- Health Research Section, Nepal Health Research Council, Kathmandu, Nepal
| | - Daniel Diaz
- Center of Complexity Sciences, National Autonomous University of Mexico, Mexico City, Mexico
- Facultad de Medicina Veterinaria y Zootecnia, Autonomous University of Sinaloa, Culiacan Rosales, Mexico
| | - Leila Doshmangir
- Department of Health Policy and Economy, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Andre R Duraes
- School of Medicine, Federal University of Bahia, Salvador, Brazil
- Diretoria Médica, Roberto Santos General Hospital, Salvador, Brazil
| | - Laura Dwyer-Lindgren
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Lucas Earl
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Roya Ebrahimi
- Environmental Health Research Center, Kurdistan University of Medical Sciences, Sanandaj, Iran
| | | | - Andem Effiong
- Clinical Epidemiology and Biostatistics, University of Newcastle, Newcastle, New South Wales, Australia
| | - Aziz Eftekhari
- Department of Toxicology and Pharmacology, Tabriz University of Medical Sciences, Tabriz, Iran
- Department of Basic Sciences, Maragheh University of Medical Sciences, Maragheh, Iran
| | - Elham Ehsani-Chimeh
- National Institute for Health Researchers, Tehran University of Medical Sciences, Tehran, Iran
| | - Iman El Sayed
- Medical Research Institute, Alexandria University, Alexandria, Egypt
| | | | - Maha El Tantawi
- Pediatric Dentistry and Dental Public Health, Alexandria University, Alexandria, Egypt
- Preventive Dental Sciences, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Ziad El-Khatib
- Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden
| | - Mohammad Hassan Emamian
- Ophthalmic Epidemiology Research Center, Shahroud University of Medical Sciences, Shahroud, Iran
| | - Shymaa Enany
- Department of Microbiology and Immunology, Suez Canal University, Ismailia, Egypt
| | - Sharareh Eskandarieh
- Multiple Sclerosis Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Oghenowede Eyawo
- Epidemiology and Population Health, British Columbia Centre for Excellence in HIV/AIDS, Vancouver, British Columbia, Canada
- Faculty of Health Sciences, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Maha Ezalarab
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | | | - Mohammad Fareed
- College of Medicine, Imam Muhammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Roghiyeh Faridnia
- Department of Parasitology, Mazandaran University of Medical Sciences, Sari, Iran
| | - Andre Faro
- Department of Psychology, Federal University of Sergipe, Sao Cristovao, Brazil
| | - Ali Akbar Fazaeli
- Social Determinants of Health Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Mehdi Fazlzadeh
- Environmental Health Engineering, Tehran University of Medical Sciences, Tehran, Iran
- Department of Environmental Health Engineering, Ardabil University of Medical Science, Ardabil, Iran
| | - Netsanet Fentahun
- Department of Public Health Nutrition, Bahir Dar University, Bahir Dar, Ethiopia
| | - Seyed-Mohammad Fereshtehnejad
- Department of Neurobiology, Karolinska Institutet, Stockholm, Sweden
- Division of Neurology, University of Ottawa, Ottawa, Ontario, Canada
| | - João C Fernandes
- Center for Biotechnology and Fine Chemistry, Catholic University of Portugal, Porto, Portugal
| | - Irina Filip
- Psychiatry Department, Kaiser Permanente, Fontana, CA, USA
- Department of Health Sciences, A.T. Still University, Mesa, AZ, USA
| | - Florian Fischer
- Department of Public Health Medicine, Bielefeld University, Bielefeld, Germany
| | - Nataliya A Foigt
- Institute of Gerontology, National Academy of Medical Sciences of Ukraine, Kyiv, Ukraine
| | | | - Joel Msafiri Francis
- Clinical Medicine and Wits Reproductive Health and HIV Institute, University of the Witwatersrand, Johannesburg, South Africa
| | - Takeshi Fukumoto
- Gene Expression & Regulation Program, Cancer Institute (W.I.A.), Philadelphia, PA, USA
- Department of Dermatology, Kobe University, Kobe, Japan
| | - Nancy Fullman
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Silvano Gallus
- Department of Environmental Health Science, Mario Negri Institute for Pharmacological Research, Milan, Italy
| | | | | | | | - Bradford D Gessner
- Vaccines Department, Pfizer, Collegeville, PA, USA
- Agency of Preventive Medicine, Paris, France
| | - Birhanu Geta
- Department of Pharmacy, Wollo University, Dessie, Ethiopia
| | - Peter W Gething
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Reza Ghadimi
- Health Research Institute, Babol University of Medical Sciences, Babol, Iran
| | | | - Mahsa Ghajarzadeh
- Department of Neurology, Tehran University of Medical Sciences, Tehran, Iran
| | - Ahmad Ghashghaee
- Department of Health Services Management, Iran University of Medical Sciences, Tehran, Iran
| | | | - Tiffany K Gill
- Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia
| | - Nick Golding
- School of BioSciences, University of Melbourne, Parkville, Victoria, Australia
| | - Nelson G M Gomes
- Department of Chemistry, University of Porto, Porto, Portugal
- REQUIMTE/LAQV, Porto, Portugal
| | - Philimon N Gona
- Nursing and Health Sciences Department, University of Massachusetts Boston, Boston, MA, USA
| | - Sameer Vali Gopalani
- Department of Biostatistics and Epidemiology, University of Oklahoma, Oklahoma City, OK, USA
- Department of Health and Social Affairs, Government of the Federated States of Micronesia, Palikir, Federated States of Micronesia
| | - Giuseppe Gorini
- Occupational and Environmental Epidemiology Section, Cancer Prevention and Research Institute, Florence, Italy
| | | | - Nicholas Graetz
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Felix Greaves
- Department of Primary Care and Public Health, Imperial College London, London, UK
- Health Improvement Directorate, Public Health England, London, UK
| | | | - Yuming Guo
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- Department of Epidemiology and Biostatistics, Zhengzhou University, Zhengzhou, China
| | - Arvin Haj-Mirzaian
- Department of Pharmacology, Tehran University of Medical Sciences, Tehran, Iran
- Obesity Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Arya Haj-Mirzaian
- Department of Pharmacology, Tehran University of Medical Sciences, Tehran, Iran
- Department of Radiology, Johns Hopkins University, Baltimore, MD, USA
| | - Brian James Hall
- Global and Community Mental Health Research Group, University of Macau, Macao, China
| | - Samer Hamidi
- School of Health and Environmental Studies, Hamdan Bin Mohammed Smart University, Dubai, United Arab Emirates
| | | | - Josep Maria Haro
- Biomedical Research Networking Center for Mental Health Network (CIBERSAM), Madrid, Spain
- Research and Development Unit, San Juan de Dios Sanitary Park, Sant Boi De Llobregat, Spain
| | - Milad Hasankhani
- School of Nutrition and Food Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Edris Hasanpoor
- Healthcare Management, Maragheh University of Medical Sciences, Maragheh, Iran
| | - Amir Hasanzadeh
- Department of Microbiology, Tehran University of Medical Sciences, Tehran, Iran
- Department of Microbiology, Maragheh University of Medical Sciences, Maragheh, Iran
| | - Hadi Hassankhani
- School of Nursing and Midwifery Tabriz University of Medical Sciences, Tabriz, Iran
- Independent Consultant, Tabriz, Iran
| | - Hamid Yimam Hassen
- Public Health Department, Mizan-Tepi University, Teppi, Ethiopia
- Unit of Epidemiology and Social Medicine, University Hospital Antwerp, Antwerp, Belgium
| | | | - Delia Hendrie
- School of Public Health, Curtin University, Bentley, Western Australia, Australia
| | - Fatemeh Heydarpour
- Medical Biology Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Thomas R Hird
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- Population Health, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Chi Linh Hoang
- Center of Excellence in Behavioral Medicine, Nguyen Tat Thanh University, Ho Chi Minh, Vietnam
| | - Gillian Hollerich
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Enayatollah Homaie Rad
- Social Determinants of Health Research Center, Guilan Road Trauma Research Center, Guilan University of Medical Sciences, Rasht, Iran
- Guilan Road Trauma Research Center, Guilan University of Medical Sciences, Rasht, Iran
| | | | - Naznin Hossain
- Health Systems and Population Studies Division, International Centre for Diarrhoeal Disease Research Bangladesh, Dhaka, Bangladesh
- Department of Pharmacology and Therapeutics, University of Dhaka, Dhaka, Bangladesh
| | - Mostafa Hosseini
- Department of Epidemiology and Biostatistics, Tehran University of Medical Sciences, Tehran, Iran
| | - Mehdi Hosseinzadeh
- Health Management and Economics Research Center, Iran University of Medical Sciences, Tehran, Iran
- Computer Science Department, University of Human Development, Sulaimaniyah, Iraq
| | - Mihaela Hostiuc
- Department of General Surgery, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
- Department of Internal Medicine, Bucharest Emergency Hospital, Bucharest, Romania
| | - Sorin Hostiuc
- Faculty of Dentistry, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
- Clinical Legal Medicine, National Institute of Legal Medicine Mina Minovici, Bucharest, Romania
| | - Mowafa Househ
- Division of Information and Computing Technology, Hamad Bin Khalifa University, Doha, Qatar
- Qatar Foundation for Education, Science and Community Development, Doha, Qatar
| | - Mohamed Hsairi
- Faculty of Medicine Tunis, Medicine School of Tunis, Baab Saadoun, Tunisia
| | | | | | - Usman Iqbal
- Global Health and Development Department, Taipei Medical University, Taipei City, Taiwan
| | - Seyed Sina Naghibi Irvani
- Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Nazrul Islam
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Harvard University, Boston, MA, USA
| | - Sheikh Mohammed Shariful Islam
- Institute for Physical Activity and Nutrition, Deakin University, Burwood, Victoria, Australia
- Sydney Medical School, University of Sydney, Sydney, New South Wales, Australia
| | - Mikk Jürisson
- Institute of Family Medicine and Public Health, University of Tartu, Tartu, Estonia
| | | | - Amir Jalali
- Psychiatric Department, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Javad Javidnia
- Department of Medical Mycology, Mazandaran University of Medical Sciences, Sari, Iran
| | - Achala Upendra Jayatilleke
- Faculty of Graduate Studies, University of Colombo, Colombo, Sri Lanka
- Institute of Medicine, University of Colombo, Colombo, Sri Lanka
| | - Ensiyeh Jenabi
- School of Midwifery, A.T. Still University, Mesa, AZ, USA
| | - John S Ji
- Environmental Research Center, Duke Kunshan University, Kunshan, China
| | | | - Kimberly Johnson
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Jost B Jonas
- Department of Ophthalmology, Heidelberg University, Heidelberg, Germany
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Beijing, China
| | - Zahra Jorjoran Shushtari
- Social Determinants of Health Research Center, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
| | - Jacek Jerzy Jozwiak
- Faculty of Medicine and Health Sciences, University of Opole, Opole, Poland
- Department of Family Medicine and Public Health, University of Opole, Opole, Poland
| | - Ali Kabir
- Minimally Invasive Surgery Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Amaha Kahsay
- Department of Nutrition and Dietetics, Mekelle University, Mekelle, Ethiopia
| | - Hamed Kalani
- Mazandaran University of Medical Sciences, Sari, Iran
- Isfahan University of Medical Sciences, Isfahan, Iran
| | - Rohollah Kalhor
- Social Determinants of Health Research Center, Qazvin University of Medical Sciences, Qazvin, Iran
| | - Manoochehr Karami
- Department of Epidemiology, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Surendra Karki
- Research and Development, Australian Red Cross Blood Service, Sydney, New South Wales, Australia
- School of Public Health and Community Medicine, University of New South Wales, Sydney, New South Wales, Australia
| | - Amir Kasaeian
- Hematologic Malignancies Research Center, Tehran University of Medical Sciences, Tehran, Iran
- Hematology-Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Nicholas J Kassebaum
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Department of Anesthesiology & Pain Medicine, University of Washington, Seattle, WA, USA
| | | | - Grant Rodgers Kemp
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Michigan State University, East Lansing, MI, USA
| | - Roghayeh Khabiri
- Tabriz Health Management Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
- National Institute for Health Research (NIHR), Tehran University of Medical Sciences, Tehran, Iran
| | - Yousef Saleh Khader
- Department of Public Health and Community Medicine, Jordan University of Science and Technology, Ramtha, Jordan
| | - Morteza Abdullatif Khafaie
- Social Determinants of Health Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Ejaz Ahmad Khan
- Epidemiology and Biostatistics Department, Health Services Academy, Islamabad, Pakistan
| | - Junaid Khan
- Population Studies, International Institute for Population Sciences, Mumbai, India
| | - Muhammad Shahzeb Khan
- Department of Internal Medicine, John H. Stroger Jr Hospital of Cook County, Chicago, IL, USA
- Department of Internal Medicine, Dow University of Health Sciences, Karachi, Pakistan
| | - Young-Ho Khang
- Institute of Health Policy and Management, Seoul National University, Seoul, South Korea
- Department of Health Policy and Management, Seoul National University, Seoul, South Korea
| | - Khaled Khatab
- Faculty of Health and Wellbeing, Sheffield Hallam University, Sheffield, UK
- Department of Arts and Sciences, Ohio University, Zanesville, OH, USA
| | - Amir Khater
- Internal Medicine and Gastroenterology Department, National Hepatology and Tropical Research Institute, Cairo, Egypt
| | - Mona M Khater
- Department of Medical Parasitology, Cairo University, Cairo, Egypt
| | | | - Mohammad Khazaei
- Department of Environmental Health Engineering, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Salman Khazaei
- Department of Epidemiology, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Maryam Khazaei-Pool
- Department of Public Health, Mazandaran University of Medical Sciences, Sari, Iran
| | - Jagdish Khubchandani
- Department of Nutrition and Health Science, Ball State University, Muncie, IN, USA
| | - Neda Kianipour
- Kermanshah University of Medical Sciences, Kermanshah, Iran
- School of Public Health, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Yun Jin Kim
- School of Medicine, Xiamen University Malaysia, Sepang, Malaysia
| | | | - Damaris K Kinyoki
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Adnan Kisa
- Department of Health Management and Health Economics, Kristiania University College, Oslo, Norway
- Department of Health Services Policy and Management, University of South Carolina, Columbia, SC, USA
| | - Sezer Kisa
- Nursing and Health Promotion, Oslo Metropolitan University, Oslo, Norway
| | - Tufa Kolola
- Department of Public Health, Debre Berhan University, Debre Berhan, Ethiopia
| | | | - Parvaiz A Koul
- Department of Internal and Pulmonary Medicine, Sheri Kashmir Institute of Medical Sciences, Srinagar, India
| | - Ai Koyanagi
- Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
- CIBERSAM, San Juan de Dios Sanitary Park, Sant Boi De Llobregat, Spain
| | - Moritz U G Kraemer
- Department of Zoology, University of Oxford, Oxford, UK
- Medical School, Harvard University, Boston, MA, USA
| | - Kewal Krishan
- Department of Anthropology, Panjab University, Chandigarh, India
| | - Kris J Krohn
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Nuworza Kugbey
- Family and Community Health, University of Health and Allied Sciences, Ho, Ghana
- Psychology and Health Promotion, University of Kwazulu-Natal, Durban, South Africa
| | - G Anil Kumar
- Public Health Foundation of India, Gurugram, India
| | - Manasi Kumar
- Department of Psychiatry, University of Nairobi, Nairobi, Kenya
- Department of Psychology, University College London, London, UK
| | | | - Desmond Kuupiel
- Department of Public Health Medicine, University of Kwazulu-Natal, Durban, South Africa
- Nursing, St John of God Hospital, Duayaw Nkwanta, Ghana
| | - Ben Lacey
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Oxford Biomedical Research Centre, National Institute for Health Research (NIHR), Oxford, UK
| | - Sheetal D Lad
- Department of Pediatrics, Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - Faris Hasan Lami
- Department of Community and Family Medicine, Academy of Medical Science, Baghdad, Iraq
| | - Anders O Larsson
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
- Department of Clinical Chemistry and Pharmacology, Uppsala University Hospital, Uppsala, Sweden
| | - Paul H Lee
- School of Nursing, Hong Kong Polytechnic University, Hong Kong, China
| | - Mostafa Leili
- Department of Environmental Health Engineering, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Aubrey J Levine
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Shanshan Li
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Lee-Ling Lim
- Department of Medicine, University of Malaya, Kuala Lumpur, Malaysia
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, China
| | - Stefan Listl
- Department of Dentistry, Radboud University, Nijmegen, The Netherlands
- Section for Translational Health Economics, Heidelberg University Hospital, Heidelberg, Germany
| | - Joshua Longbottom
- Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, UK
| | - Jaifred Christian F Lopez
- Department of Epidemiology and Biostatistics, University of the Philippines Manila, Manila, The Philippines
- Alliance for Improving Health Outcomes, Quezon City, The Philippines
| | - Stefan Lorkowski
- Institute of Nutrition, Friedrich Schiller University Jena, Jena, Germany
- Competence Cluster for Nutrition and Cardiovascular Health (NUTRICARD), Jena, Germany
| | - Sameh Magdeldin
- Physiology Department, Suez Canal University, Ismailia, Egypt
- Proteomics and Metabolomics Unit, Suez Canal University, Ismailia, Egypt
| | | | | | - Azeem Majeed
- Department of Primary Care and Public Health, Imperial College London, London, UK
| | - Afshin Maleki
- Environmental Health Research Center, Kurdistan University of Medical Sciences, Sanandaj, Iran
| | - Reza Malekzadeh
- Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
- Non-communicable Diseases Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Deborah Carvalho Malta
- Department of Maternal and Child Nursing and Public Health, Federal University of Minas Gerais, Belo Horizonte, Brazil
| | - Abdullah A Mamun
- Institute for Social Science Research, The University of Queensland, Brisbane, Queensland, Australia
| | - Navid Manafi
- Ophthalmology Department, Iran University of Medical Sciences, Tehran, Iran
- Department Ophthalmology, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Ana-Laura Manda
- Surgery Department, Emergency University Hospital Bucharest, Bucharest, Romania
| | - Morteza Mansourian
- Department of Health Education and Health Promotion, Iran University of Medical Sciences, Tehran, Iran
| | | | - Anthony Masaka
- Faculty of Health and Education, Botho University-Botswana, Gaborone, Botswana
| | | | - Pallab K Maulik
- School of Medicine, University of New South Wales, Sydney, New South Wales, Australia
- Research Department, The George Institute for Global Health, New Delhi, India
| | - Benjamin K Mayala
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Mohsen Mazidi
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Martin McKee
- Department of Health Services Research and Policy, London School of Hygiene & Tropical Medicine, London, UK
| | - Ravi Mehrotra
- Preventive Oncology Department, National Institute of Cancer Prevention and Research, Noida, India
| | - Kala M Mehta
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
| | | | - Walter Mendoza
- Peru Country Office, United Nations Population Fund (UNFPA), Lima, Peru
| | - Ritesh G Menezes
- Forensic Medicine Division, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Atte Meretoja
- Neurocenter, Helsinki University Hospital, Helsinki, Finland
- School of Health Sciences, University of Melbourne, Parkville, Victoria, Australia
| | - Tuomo J Meretoja
- Breast Surgery Unit, Helsinki University Hospital, Helsinki, Finland
- University of Helsinki, Helsinki, Finland
| | - Tomislav Mestrovic
- Clinical Microbiology and Parasitology Unit, Dr Zora Profozic Polyclinic, Zagreb, Croatia
- University Centre Varazdin, University North, Varazdin, Croatia
| | - Ted R Miller
- School of Public Health, Curtin University, Bentley, Western Australia, Australia
- Pacific Institute for Research & Evaluation, Calverton, MD, USA
| | - Molly K Miller-Petrie
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Edward J Mills
- Health, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada
| | - George J Milne
- Department of Computer Science and Software Engineering, University of Western Australia, Perth, Western Australia, Australia
| | - G K Mini
- Department of Public Health, Amrita Institute of Medical Sciences, Kochi, India
| | - Seyed Mostafa Mir
- Department of Clinical Biochemistry, Babol University of Medical Sciences, Babol, Iran
- Golestan University of Medical Sciences, Gorgan, Iran
| | - Hamed Mirjalali
- Foodborne and Waterborne Diseases Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Erkin M Mirrakhimov
- Faculty of General Medicine, Kyrgyz State Medical Academy, Bishkek, Kyrgyzstan
- Department of Atherosclerosis and Coronary Heart Disease, National Center of Cardiology and Internal Disease, Bishkek, Kyrgyzstan
| | - Efat Mohamadi
- Health Equity Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Dara K Mohammad
- Department of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden
- Department of Food Technology, College of Agriculture, Salahaddin University-Erbil, Erbil, Iraq
| | - Aso Mohammad Darwesh
- Information Technology Department, University of Human Development, Sulaimaniyah, Iraq
| | | | | | - Shafiu Mohammed
- Institute of Public Health, Heidelberg University, Heidelberg, Germany
- Health Systems and Policy Research Unit, Ahmadu Bello University, Zaria, Nigeria
| | - Ali H Mokdad
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Mariam Molokhia
- Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Lorenzo Monasta
- Clinical Epidemiology and Public Health Research Unit, Burlo Garofolo Institute for Maternal and Child Health, Trieste, Italy
| | - Yoshan Moodley
- Department of Public Health Medicine, University of Kwazulu-Natal, Durban, South Africa
| | - Mahmood Moosazadeh
- Health Sciences Research Center, Mazandaran University of Medical Sciences, Sari, Iran
| | - Ghobad Moradi
- Department of Epidemiology and Biostatistics, Kurdistan University of Medical Sciences, Sanandaj, Iran
- Social Determinants of Health Research Center, Kurdistan University of Medical Sciences, Sanandaj, Iran
| | - Masoud Moradi
- Kermanshah University of Medical Sciences, Kermanshah, Iran
- Research Center for Environmental Determinants of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Yousef Moradi
- Department of Epidemiology, Iran University of Medical Sciences, Tehran, Iran
| | - Maziar Moradi-Lakeh
- Preventive Medicine and Public Health Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Mehdi Moradinazar
- Research Center for Environmental Determinants of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Paula Moraga
- Department of Mathematical Sciences, University of Bath, Bath, UK
| | - Lidia Morawska
- International Laboratory for Air Quality and Health, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Abbas Mosapour
- Department of Clinical Biochemistry, Babol University of Medical Sciences, Babol, Iran
- Department of Clinical Biochemistry, Tarbiat Modares University, Tehran, Iran
| | - Seyyed Meysam Mousavi
- Department of Health Management and Economics, Tehran University of Medical Sciences, Tehran, Iran
| | - Ulrich Otto Mueller
- Federal Institute for Population Research, Wiesbaden, Germany
- Center for Population and Health, Wiesbaden, Germany
| | - Atalay Goshu Muluneh
- Department of Epidemiology and Biostatistics, University of Gondar, Gondar, Ethiopia
| | - Ghulam Mustafa
- Department of Pediatric Medicine, Nishtar Medical University, Multan, Pakistan
- Department of Pediatrics & Pediatric Pulmonology, Institute of Mother & Child Care, Multan, Pakistan
| | - Behnam Nabavizadeh
- Department of Urology, Tehran University of Medical Sciences, Tehran, Iran
| | - Mehdi Naderi
- Operating Room Department, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Ahamarshan Jayaraman Nagarajan
- Research and Analytics, Initiative for Financing Health and Human Development, Chennai, India
- Research and Analytics, Bioinsilico Technologies, Chennai, India
| | - Azin Nahvijou
- Cancer Research Center of Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Farid Najafi
- Department of Epidemiology & Biostatistics, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | | | | | - Nahid Neamati
- Department of Clinical Biochemistry, Babol University of Medical Sciences, Babol, Iran
| | - Ionut Negoi
- Emergency Hospital of Bucharest, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
- General Surgery Department, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
| | - Ruxandra Irina Negoi
- Anatomy and Embryology Department, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
- Department of Cardiology, Cardio-aid, Bucharest, Romania
| | | | | | - Long Hoang Nguyen
- Center for Excellence in Behavioral Health, Nguyen Tat Thanh University, Ho Chi Minh, Vietnam
| | - Son Hoang Nguyen
- Center for Excellence in Behavioral Health, Nguyen Tat Thanh University, Ho Chi Minh, Vietnam
| | - Katie R Nielsen
- Global Health Department, University of Washington, Seattle, WA, USA
- Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Dina Nur Anggraini Ningrum
- State University of Semarang, Public Health Science Department, Kota Semarang, Indonesia
- Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei City, Taiwan
| | | | - Molly R Nixon
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Chukwudi A Nnaji
- Cochrane South Africa, South African Medical Research Council, Cape Town, South Africa
- Public Health Science Department, School of Public Health and Family Medicine, University of Cape Town, Cape Town, South Africa
| | - Marzieh Nojomi
- Preventive Medicine and Public Health Research Center, Iran University of Medical Sciences, Tehran, Iran
- Department of Community and Family Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Mehdi Noroozi
- University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
| | - Shirin Nosratnejad
- Department of Health Economics, Tabriz University of Medical Sciences, Tabriz, Iran
| | | | | | - Richard Ofori-Asenso
- Centre of Cardiovascular Research and Education in Therapeutics, Monash University, Melbourne, Victoria, Australia
- Independent Consultant, Accra, Ghana
| | - Felix Akpojene Ogbo
- Translational Health Research Institute, Western Sydney University, Penrith, New South Wales, Australia
| | - Kelechi E Oladimeji
- Department of Public Health Medicine, University of Kwazulu-Natal, Durban, South Africa
- Center for the Aid Program of Research in South Africa (CAPRISA) TB and HIV Pathogenesis Unit, United Nations Programme on HIV/AIDS (UNAIDS), Durban, South Africa
| | - Andrew T Olagunju
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
- Department of Psychiatry, University of Lagos, Lagos, Nigeria
| | - Meysam Olfatifar
- Gastroenterology and Liver Disease Research Center, A.C.S. Medical College and Hospital, Tehran, Iran
| | - Solomon Olum
- Department of Food Science and Postharvest Technology, Gulu University, Gulu, Uganda
- Ghent University, Ghent, Belgium
| | | | | | - Obinna E Onwujekwe
- Department of Pharmacology and Therapeutics, University of Nigeria Nsukka, Enugu, Nigeria
| | - Eyal Oren
- University of Washington, Seattle, WA, USA
- Graduate School of Public Health, San Diego State University, San Diego, CA, USA
| | | | - Alberto Ortiz
- School of Medicine, Autonomous University of Madrid, Madrid, Spain
- Department of Nephrology and Hypertension, The Institute for Health Research Foundation Jiménez Díaz University Hospital, Madrid, Spain
| | | | - Frank B Osei
- Faculty of Geoinformation Science and Earth Observation, University of Twente, Enschede, The Netherlands
- Department of Mathematics and Statistics, University of Energy and Natural Resources, Sunyani, Ghana
| | | | - Stanislav S Otstavnov
- Analytical Center, Moscow Institute of Physics and Technology, Dolgoprudny, Russia
- Committee for the Comprehensive Assessment of Medical Devices and Information Technology, Health Technology Assessment Association, Moscow, Russia
| | - Mayowa Ojo Owolabi
- Institute for Advanced Medical Research and Training, University of Ibadan, Ibadan, Nigeria
| | - Mahesh P A
- Department of Tb & Respiratory Medicine, Jagadguru Sri Shivarathreeswara University, Mysore, India
| | - Abdol Sattar Pagheh
- Toxoplasmosis Research Center, Mazandaran University of Medical Sciences, Sari, Iran
| | - Smita Pakhale
- Department of Medicine, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | | | | | - Eun-Kee Park
- Department of Medical Humanities and Social Medicine, Kosin University, Busan, South Korea
| | - Hadi Parsian
- Department of Clinical Biochemistry, Babol University of Medical Sciences, Babol, Iran
| | - Tahereh Pashaei
- Environmental Health Research Center, Kurdistan University of Medical Sciences, Sanandaj, Iran
| | - Sangram Kishor Patel
- Research and Evaluation, Population Council, New Delhi, India
- Indian Institute of Health Management Research University, Jaipur, India
| | | | - Alexandre Pereira
- Department of Genetics, Harvard University, Boston, MA, USA
- Laboratory of Genetics and Molecular Cardiology, University of São Paulo, Sao Paulo, Brazil
| | - Samantha Perkins
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Brandon V Pickering
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Thomas Pilgrim
- Department of Cardiology, University of Bern, Bern, Switzerland
| | - Majid Pirestani
- Parasitology and Entomology Department, Tarbiat Modares University, Tehran, Iran
| | - Bakhtiar Piroozi
- Social Determinants of Health Research Center, Kurdistan University of Medical Sciences, Sanandaj, Iran
| | | | | | - Hadi Pourjafar
- Department of Nutrition and Food Sciences, Maragheh University of Medical Sciences, Maragheh, Iran
- Department of Public Health, Maragheh University of Medical Sciences, Maragheh, Iran
| | - Parul Puri
- International Institute for Population Sciences, Mumbai, India
| | - Mostafa Qorbani
- Non-communicable Diseases Research Center, Alborz University of Medical Sciences, Karaj, Iran
| | - Hedley Quintana
- Gorgas Memorial Institute for Health Studies, Panama, Panama
| | - Mohammad Rabiee
- Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
| | - Navid Rabiee
- Department of Chemistry, Sharif University of Technology, Tehran, Iran
| | - Amir Radfar
- College of Graduate Health Sciences, A.T. Still University, Mesa, AZ, USA
- Medichem, Barcelona, Spain
| | - Alireza Rafiei
- Molecular and Cell Biology Research Center, Mazandaran University of Medical Sciences, Sari, Iran
- Department of Immunology, Mazandaran University of Medical Sciences, Sari, Iran
| | - Fakher Rahim
- Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
- Thalassemia and Hemoglobinopathy Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Zohreh Rahimi
- Department of Clinical Biochemistry, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Vafa Rahimi-Movaghar
- Sina Trauma and Surgery Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Shadi Rahimzadeh
- Non-communicable Diseases Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Fatemeh Rajati
- Department of Health Education & Promotion, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Sree Bhushan Raju
- Department of Nephrology, Nizam's Institute of Medical Sciences, Hyderabad, India
| | - Azra Ramezankhani
- Prevention of Metabolic Disorders Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Critical Care Quality Improvement Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Chhabi Lal Ranabhat
- Policy Research Institute, Kathmandu, Nepal
- Institute for Poverty Alleviation and International Development, Yonsei University, Wonju, South Korea
| | - Davide Rasella
- Institute of Public Health, Federal University of Bahia, Salvador, Brazil
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, Brazil
| | - Vahid Rashedi
- School of Behavioral Sciences and Mental Health, Iran University of Medical Sciences, Tehran, Iran
| | - Lal Rawal
- School of Science and Health, Western Sydney University, Penrith, New South Wales, Australia
- Social Science and Psychology, Western Sydney University, Penrith, New South Wales, Australia
| | - Robert C Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Andre M N Renzaho
- School of Social Sciences and Psychology, Western Sydney University, Penrith, New South Wales, Australia
| | - Satar Rezaei
- Research Center for Environmental Determinants of Health (RCEDH), Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Aziz Rezapour
- Health Management and Economics Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Seyed Mohammad Riahi
- Department of Epidemiology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Department of Epidemiology, Birjand University of Medical Sciences, Birjand, Iran
| | | | - Leonardo Roever
- Department of Clinical Research, Federal University of Uberlândia, Uberlândia, Brazil
| | - Elias Merdassa Roro
- Public Health, Addis Ababa University, Addis Ababa, Ethiopia
- Department of Public Health, Wollega University, Nekemte, Ethiopia
| | - Max Roser
- Martin School, University of Oxford, Oxford, UK
| | - Gholamreza Roshandel
- Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
- Golestan Research Center of Gastroenterology and Hepatology, Golestan University of Medical Sciences, Gorgan, Iran
| | - Daem Roshani
- Epidemiology and Biostatistics, Kurdistan University of Medical Sciences, Sanandaj, Iran
| | - Ali Rostami
- Infectious Diseases and Tropical Medicine Research Center, Babol University of Medical Sciences, Babol, Iran
| | - Enrico Rubagotti
- School of Biotechnology, Ikiam Amazon Regional University, Tena, Ecuador
- Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Salvatore Rubino
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Siamak Sabour
- Department of Epidemiology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Nafis Sadat
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Ehsan Sadeghi
- Research Center for Environmental Determinants of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Reza Saeedi
- Department of Health, Safety and Environment (HSE), Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Yahya Safari
- Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Roya Safari-Faramani
- Faculty of Public Health, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Mahdi Safdarian
- Sina Trauma and Surgery Research Center, Tehran University of Medical Sciences, Tehran, Iran
- Department of Neuroscience, Iran University of Medical Sciences, Tehran, Iran
| | - Amirhossein Sahebkar
- Neurogenic Inflammation Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Biotechnology Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | | | - Nasir Salam
- Department of Pathology, Al-Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Payman Salamati
- Sina Trauma and Surgery Research Center, Tehran University of Medical Sciences, Tehran, Iran
- School of Health and Policy Management, Faculty of Health, York University, Toronto, Ontario, Canada
| | - Farkhonde Salehi
- Taleghani Hospital, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Saleh Salehi Zahabi
- Department of Radiology and Nuclear Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran
- Taleghani Hospital, Kermanshah, Iran
| | - Yahya Salimi
- Department of Epidemiology & Biostatistics, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Hamideh Salimzadeh
- Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Joshua A Salomon
- Center for Health Policy & Center for Primary Care and Outcomes Research, Stanford University, Stanford, CA, USA
| | | | - Abdallah M Samy
- Department of Entomology, Ain Shams University, Cairo, Egypt
| | | | - Bruno Piassi Sao Jose
- Post-graduate Program in Infectious Diseases and Tropical Medicine, Federal University of Minas Gerais, Belo Horizonte, Brazil
| | - Sivan Yegnanarayana Iyer Saraswathy
- Department of Community Medicine, PSG Institute of Medical Sciences and Research, Coimbatore, India
- PSG-FAIMER South Asia Regional Institute, Coimbatore, India
| | - Rodrigo Sarmiento-Suárez
- Department of Health and Society, Faculty of Medicine, University of Applied and Environmental Sciences, Bogotá, Colombia
| | - Benn Sartorius
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Brijesh Sathian
- Surgery Department, Hamad Medical Corporation, Doha, Qatar
- Faculty of Health & Social Sciences, Bournemouth University, Bournemouth, UK
| | - Sonia Saxena
- School of Public Health, Imperial College London, London, UK
| | - Alyssa N Sbarra
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Lauren E Schaeffer
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - David C Schwebel
- Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Sadaf G Sepanlou
- Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
- Non-communicable Diseases Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Seyedmojtaba Seyedmousavi
- Center of Expertise in Microbiology, Tehran University of Medical Sciences, Tehran, Iran
- Invasive Fungi Research Center, Mazandaran University of Medical Sciences, Sari, Iran
| | - Faramarz Shaahmadi
- Department of Health Promotion and Education, Alborz University of Medical Sciences, Karaj, Iran
| | | | - Mehran Shams-Beyranvand
- Non-communicable Diseases Research Center, Tehran University of Medical Sciences, Tehran, Iran
- School of Medicine, Dezful University of Medical Sciences, Dezful, Iran
| | - Amir Shamshirian
- Medical Laboratory Sciences, Mazandaran University of Medical Sciences, Sari, Iran
| | - Morteza Shamsizadeh
- Chronic Diseases (Home Care) Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
| | | | - Mehdi Sharif
- Department of Laboratory Sciences, Karaj Islamic Azad University, Kermanshah, Iran
- Department of Basic Sciences, Karaj Islamic Azad University, Kermanshah, Iran
| | - Mahdi Sharif-Alhoseini
- Sina Trauma and Surgery Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Hamid Sharifi
- HIV/STI Surveillance Research Center, Kerman University of Medical Sciences, Kerman, Iran
| | - Jayendra Sharma
- Policy and Planning Division, Ministry of Health, Riyadh, Saudi Arabia
| | - Rajesh Sharma
- University School of Management and Entrepreneurship, Delhi Technological University, New Delhi, India
| | - Aziz Sheikh
- Division of General Internal Medicine and Primary Care, Harvard University, Boston, MA, USA
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Chloe Shields
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | | | - Rahman Shiri
- Finnish Institute of Occupational Health, Helsinki, Finland
| | - Ivy Shiue
- Institute of Medical Epidemiology, Martin Luther University Halle-Wittenberg, Halle, Germany
| | - Kerem Shuval
- School of Public Health, University of Haifa, Haifa, Israel
| | - Tariq J Siddiqi
- Department of Internal Medicine, Dow University of Health Sciences, Karachi, Pakistan
| | - João Pedro Silva
- Applied Molecular Biosciences Unit (UCIBIO), University of Porto, Porto, Portugal
| | - Jasvinder A Singh
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Dhirendra Narain Sinha
- Department of Epidemiology, School of Preventive Oncology, Patna, India
- Department of Epidemiology, Healis Sekhsaria Institute for Public Health, Mumbai, India
| | - Malede Mequanent Sisay
- Department of Epidemiology and Biostatistics, University of Gondar, Gondar, Ethiopia
- Department of Physiotherapy and Occupational Therapy, Næstved-Slagelse-Ringsted Hospitals, Slagelse, Denmark
| | - Solomon Sisay
- Medical Division, German Leprosy and TB Relief Association Ethiopia, Addis Ababa, Ethiopia
| | - Karen Sliwa
- Department of Medicine, University of Cape Town, Cape Town, South Africa
| | - David L Smith
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Ranjani Somayaji
- Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Moslem Soofi
- Social Development and Health Promotion Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Joan B Soriano
- Hospital Universitario de la Princesa, Autonomous University of Madrid, Madrid, Spain
- Centro de Investigación en Red de Enfermedades Respiratorias (CIBERES), Institute of Health Carlos III, Madrid, Spain
| | | | - Agus Sudaryanto
- Department of Nursing, Muhammadiyah University of Surakarta, Kartasura, Indonesia
| | | | - Bryan L Sykes
- Department of Criminology, Law and Society, University of California Irvine, Irvine, CA, USA
| | - P N Sylaja
- Neurology Department, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, India
| | - Rafael Tabarés-Seisdedos
- Carlos III Health Institute, Biomedical Research Networking Center for Mental Health Network (CIBERSAM), Madrid, Spain
- Department of Medicine, University of Valencia, Valencia, Spain
| | - Karen M Tabb
- School of Social Work, University of Illinois, Urbana, IL, USA
| | - Takahiro Tabuchi
- Cancer Control Center, Osaka International Cancer Institute, Osaka, Japan
| | - Nuno Taveira
- University Institute 'Egas Moniz', Monte Da Caparica, Portugal
- Research Institute for Medicines, Faculty of Pharmacy of Lisbon, University of Lisbon, Lisbon, Portugal
| | - Mohamad-Hani Temsah
- Department of Pediatrics, King Saud University, Riyadh, Saudi Arabia
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
| | - Abdullah Sulieman Terkawi
- Anesthesiology Department, University of Virginia, Charlottesville, VA, USA
- Syrian Expatriate Medical Association (SEMA), Charlottesville, VA, USA
| | | | | | | | - Quyen G To
- School of Exercise and Nutrition Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | | | - Bach Xuan Tran
- Department of Health Economics, Hanoi Medical University, Hanoi, Vietnam
| | - Khanh Bao Tran
- Department of Molecular Medicine and Pathology, University of Auckland, Auckland, New Zealand
- Clinical Hematology and Toxicology, Military Medical University, Hanoi, Vietnam
| | - Irfan Ullah
- Gomal Center of Biochemistry and Biotechnology, Gomal University, Dera Ismail Khan, Pakistan
- TB Culture Laboratory, Mufti Mehmood Memorial Teaching Hospital, Dera Ismail Khan, Pakistan
| | - Muhammad Shariq Usman
- Department of Internal Medicine, Dow University of Health Sciences, Karachi, Pakistan
| | | | - Amir Vahedian-Azimi
- Department of Education and Health, Trauma Research Center, Tehran, Iran
- Critical and Intensive Care Department, Trauma Research Center, Tehran, Iran
| | - Pascual R Valdez
- Argentine Society of Medicine, Buenos Aires, Argentina
- Velez Sarsfield Hospital, Buenos Aires, Argentina
| | - Job F M van Boven
- University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of General Practice, University Medical Center Groningen, Groningen, The Netherlands
| | | | - Yasser Vasseghian
- Research Center for Environmental Determinants of Health (RCEDH), Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Yousef Veisani
- Psychosocial Injuries Research Center, Ilam University of Medical Sciences, Ilam, Iran
| | - Narayanaswamy Venketasubramanian
- Raffles Neuroscience Centre, Raffles Hospital, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Francesco S Violante
- Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
- Occupational Health Unit, Sant'orsola Malpighi Hospital, Bologna, Italy
| | - Sergey Konstantinovitch Vladimirov
- Department of Information Technologies and Management, Moscow Institute of Physics and Technology, Dolgoprudny, Russia
- Department of Information and Internet Technologies, I. M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Vasily Vlassov
- Department of Health Care Administration and Economy, National Research University Higher School of Economics, Moscow, Russia
| | - Theo Vos
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Giang Thu Vu
- Center of Excellence in Behavioral Medicine, Nguyen Tat Thanh University, Ho Chi Minh, Vietnam
| | | | - Yasir Waheed
- Foundation University Medical College, Foundation University, Rawalpindi, Pakistan
| | - Jon Wakefield
- Department of Statistics, University of Washington, Seattle, WA, USA
| | - Haidong Wang
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Yafeng Wang
- Department of Epidemiology and Biostatistics, Wuhan University, Wuhan, China
| | - Yuan-Pang Wang
- Department of Psychiatry, University of São Paulo, Sao Paulo, Brazil
| | - Joseph L Ward
- Institute of Child Health, University College London, London, UK
| | - Robert G Weintraub
- Cardiology Department, Royal Children's Hospital, Melbourne, Victoria, Australia
- Murdoch Childrens Research Institute, Melbourne, Victoria, Australia
| | | | | | - Ronny Westerman
- Competence Center of Mortality-Follow-Up, Federal Institute for Population Research, Wiesbaden, Germany
| | - Charles Shey Wiysonge
- Cochrane South Africa, Medical Research Council South Africa, Cape Town, South Africa
- Department of Global Health, Stellenbosch University, Cape Town, South Africa
| | - Dawit Zewdu Wondafrash
- Department of Pharmacology and Toxicology, Mekelle University, Mekelle, Ethiopia
- Department of Pharmacology, Addis Ababa University, Addis Ababa, Ethiopia
| | - Lauren Woyczynski
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Ai-Min Wu
- Zhejiang Spine Research Center, Wenzhou Medical University, Wenzhou, China
| | - Gelin Xu
- School of Medicine, Nanjing University, Nanjing, China
| | - Abbas Yadegar
- Foodborne and Waterborne Diseases Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Tomohide Yamada
- Department of Diabetes and Metabolic Diseases, University of Tokyo, Tokyo, Japan
| | - Vahid Yazdi-Feyzabadi
- Department of Health Management, Policy and Economics, Kerman University of Medical Sciences, Kerman, Iran
- Health Services Management Research Center, Kerman University of Medical Sciences, Kerman, Iran
| | - Christopher Sabo Yilgwan
- Department of Pediatrics, University of Jos, Jos, Nigeria
- Department of Pediatrics, Jos University Teaching Hospital, Jos, Nigeria
| | - Paul Yip
- Centre for Suicide Research and Prevention, University of Hong Kong, Hong Kong, China
- Department of Social Work and Social Administration, University of Hong Kong, Hong Kong, China
| | - Naohiro Yonemoto
- Department of Psychopharmacology, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Javad Yoosefi Lebni
- Department of Health Education and Health Promotion, Iran University of Medical Sciences, Tehran, Iran
| | - Mustafa Z Younis
- Health Economics & Finance, Global Health, Jackson State University, Jackson, MS, USA
- Research Center for Public Health, Tsinghua University, Peking, China
| | - Mahmoud Yousefifard
- Prevention of Cardiovascular Disease Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Chuanhua Yu
- Department of Epidemiology and Biostatistics, Wuhan University, Wuhan, China
- Global Health Institute, Wuhan University, Wuhan, China
| | - Hasan Yusefzadeh
- Department of Health Management and Economics, A.C.S. Medical College and Hospital, Tehran, Iran
| | - Erfan Zabeh
- Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
- Electrical Engineering, Institute for Research in Fundamental Sciences, Tehran, Iran
| | - Telma Zahirian Moghadam
- Health Management and Economics Research Center, Iran University of Medical Sciences, Tehran, Iran
- Social Determinants of Health Research Center, Ardabil University of Medical Science, Ardabil, Iran
| | - Sojib Bin Zaman
- Maternal and Child Health Division, International Centre for Diarrhoeal Disease Research Bangladesh, Dhaka, Bangladesh
- Department of Medicine, Monash University, Melbourne, Victoria, Australia
| | - Mohammad Zamani
- Student Research Committee, Babol University of Medical Sciences, Babol, Iran
| | - Hamed Zandian
- Social Determinants of Health Research Center, Ardabil University of Medical Science, Ardabil, Iran
- Department of Community Medicine, Ardabil University of Medical Science, Ardabil, Iran
| | - Alireza Zangeneh
- Social Development and Health Promotion Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Taddese Alemu Zerfu
- Maternal and Child Wellbeing Unit, African Population Health Research Centre, Nairobi, Kenya
- Public Health Department, Dilla University, Dilla, Ethiopia
| | - Yunquan Zhang
- Department of Preventative Medicine, Wuhan University, Wuhan, China
- School of Public Health, Wuhan University of Science and Technology, Wuhan, China
| | - Arash Ziapour
- Department of Health Education and Health Promotion, Iran University of Medical Sciences, Tehran, Iran
| | - Sanjay Zodpey
- Indian Institute of Public Health, Public Health Foundation of India, Gurugram, India
| | - Christopher J L Murray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Simon I Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA.
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27
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Reiner RC, Welgan CA, Casey DC, Troeger CE, Baumann MM, Nguyen QP, Swartz SJ, Blacker BF, Deshpande A, Mosser JF, Osgood-Zimmerman AE, Earl L, Marczak LB, Munro SB, Miller-Petrie MK, Rodgers Kemp G, Frostad J, Wiens KE, Lindstedt PA, Pigott DM, Dwyer-Lindgren L, Ross JM, Burstein R, Graetz N, Rao PC, Khalil IA, Davis Weaver N, Ray SE, Davis I, Farag T, Brady OJ, Kraemer MUG, Smith DL, Bhatt S, Weiss DJ, Gething PW, Kassebaum NJ, Mokdad AH, Murray CJL, Hay SI. Identifying residual hotspots and mapping lower respiratory infection morbidity and mortality in African children from 2000 to 2017. Nat Microbiol 2019; 4:2310-2318. [PMID: 31570869 PMCID: PMC6877470 DOI: 10.1038/s41564-019-0562-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Accepted: 08/15/2019] [Indexed: 12/13/2022]
Abstract
Lower respiratory infections (LRIs) are the leading cause of death in children under the age of 5, despite the existence of vaccines against many of their aetiologies. Furthermore, more than half of these deaths occur in Africa. Geospatial models can provide highly detailed estimates of trends subnationally, at the level where implementation of health policies has the greatest impact. We used Bayesian geostatistical modelling to estimate LRI incidence, prevalence and mortality in children under 5 subnationally in Africa for 2000–2017, using surveys covering 1.46 million children and 9,215,000 cases of LRI. Our model reveals large within-country variation in both health burden and its change over time. While reductions in childhood morbidity and mortality due to LRI were estimated for almost every country, we expose a cluster of residual high risk across seven countries, which averages 5.5 LRI deaths per 1,000 children per year. The preventable nature of the vast majority of LRI deaths mandates focused health system efforts in specific locations with the highest burden. Geospatial modelling shows an overall decline in morbidity and mortality due to lower respiratory infections in Africa from 2000 to 2017, but also identifies subnational areas with residual high risk.
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Affiliation(s)
- Robert C Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA. .,Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA.
| | - Catherine A Welgan
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Daniel C Casey
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Christopher E Troeger
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Mathew M Baumann
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - QuynhAnh P Nguyen
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Scott J Swartz
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Brigette F Blacker
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Aniruddha Deshpande
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Jonathan F Mosser
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | | | - Lucas Earl
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Laurie B Marczak
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Sandra B Munro
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Molly K Miller-Petrie
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Grant Rodgers Kemp
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.,Michigan State University, East Lansing, MI, USA
| | - Joseph Frostad
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Kirsten E Wiens
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Paulina A Lindstedt
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - David M Pigott
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.,Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Laura Dwyer-Lindgren
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.,Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Jennifer M Ross
- Department of Global Health, University of Washington, Seattle, WA, USA
| | - Roy Burstein
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Nicholas Graetz
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Puja C Rao
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Ibrahim A Khalil
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.,Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Nicole Davis Weaver
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Sarah E Ray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Ian Davis
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Tamer Farag
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Oliver J Brady
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Moritz U G Kraemer
- Department of Zoology, University of Oxford, Oxford, UK.,Harvard Medical School, University of Harvard, Boston, MA, USA
| | - David L Smith
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.,Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | | | | | | | - Nicholas J Kassebaum
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.,Department of Anesthesiology & Pain Medicine, University of Washington, Seattle, WA, USA
| | - Ali H Mokdad
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.,Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Christopher J L Murray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.,Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Simon I Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA. .,Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA.
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28
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Hendriks CMJ, Gibson HS, Trett A, Python A, Weiss DJ, Vrieling A, Coleman M, Gething PW, Hancock PA, Moyes CL. Mapping Geospatial Processes Affecting the Environmental Fate of Agricultural Pesticides in Africa. Int J Environ Res Public Health 2019; 16:E3523. [PMID: 31547208 PMCID: PMC6801543 DOI: 10.3390/ijerph16193523] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 09/15/2019] [Accepted: 09/16/2019] [Indexed: 11/16/2022]
Abstract
The application of agricultural pesticides in Africa can have negative effects on human health and the environment. The aim of this study was to identify African environments that are vulnerable to the accumulation of pesticides by mapping geospatial processes affecting pesticide fate. The study modelled processes associated with the environmental fate of agricultural pesticides using publicly available geospatial datasets. Key geospatial processes affecting the environmental fate of agricultural pesticides were selected after a review of pesticide fate models and maps for leaching, surface runoff, sedimentation, soil storage and filtering capacity, and volatilization were created. The potential and limitations of these maps are discussed. We then compiled a database of studies that measured pesticide residues in Africa. The database contains 10,076 observations, but only a limited number of observations remained when a standard dataset for one compound was extracted for validation. Despite the need for more in-situ data on pesticide residues and application, this study provides a first spatial overview of key processes affecting pesticide fate that can be used to identify areas potentially vulnerable to pesticide accumulation.
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Affiliation(s)
- Chantal M J Hendriks
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK.
- Team Sustainable Soil Use, Wageningen Environmental Research, P.O. Box 47, 6700 AA Wageningen, The Netherlands.
| | - Harry S Gibson
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK.
| | - Anna Trett
- Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool L3 5QA, UK.
| | - André Python
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK.
| | - Daniel J Weiss
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK.
| | - Anton Vrieling
- Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands.
| | - Michael Coleman
- Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool L3 5QA, UK.
| | - Peter W Gething
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK.
| | - Penny A Hancock
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK.
| | - Catherine L Moyes
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK.
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29
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Cooper L, Kang SY, Bisanzio D, Maxwell K, Rodriguez-Barraquer I, Greenhouse B, Drakeley C, Arinaitwe E, G Staedke S, Gething PW, Eckhoff P, Reiner RC, Hay SI, Dorsey G, Kamya MR, Lindsay SW, Grenfell BT, Smith DL. Pareto rules for malaria super-spreaders and super-spreading. Nat Commun 2019; 10:3939. [PMID: 31477710 PMCID: PMC6718398 DOI: 10.1038/s41467-019-11861-y] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 08/05/2019] [Indexed: 11/09/2022] Open
Abstract
Heterogeneity in transmission is a challenge for infectious disease dynamics and control. An 80-20 "Pareto" rule has been proposed to describe this heterogeneity whereby 80% of transmission is accounted for by 20% of individuals, herein called super-spreaders. It is unclear, however, whether super-spreading can be attributed to certain individuals or whether it is an unpredictable and unavoidable feature of epidemics. Here, we investigate heterogeneous malaria transmission at three sites in Uganda and find that super-spreading is negatively correlated with overall malaria transmission intensity. Mosquito biting among humans is 90-10 at the lowest transmission intensities declining to less than 70-30 at the highest intensities. For super-spreaders, biting ranges from 70-30 down to 60-40. The difference, approximately half the total variance, is due to environmental stochasticity. Super-spreading is thus partly due to super-spreaders, but modest gains are expected from targeting super-spreaders.
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Affiliation(s)
- Laura Cooper
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.,Department of Veterinary Medicine, Cambridge University, Cambridge, UK
| | - Su Yun Kang
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Donal Bisanzio
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.,RTI International, Washington, DC, USA.,Epidemiology and Public Health Division, School of Medicine, University of Nottingham, Nottingham, UK
| | - Kilama Maxwell
- Infectious Diseases Research Collaboration, Kampala, Uganda
| | - Isabel Rodriguez-Barraquer
- Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA.,Department of Medicine, University of California, San Francisco, CA, USA
| | - Bryan Greenhouse
- Department of Medicine, University of California, San Francisco, CA, USA
| | - Chris Drakeley
- London School of Hygiene & Tropical Medicine, London, UK
| | - Emmanuel Arinaitwe
- Infectious Diseases Research Collaboration, Kampala, Uganda.,London School of Hygiene & Tropical Medicine, London, UK
| | | | - Peter W Gething
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | | | - Robert C Reiner
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA.,Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Simon I Hay
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA.,Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Grant Dorsey
- Department of Medicine, University of California, San Francisco, CA, USA
| | - Moses R Kamya
- School of Medicine, Makerere University College of Health Sciences, Kampala, Uganda
| | - Steven W Lindsay
- School of Biological and Biomedical Sciences, Durham University, Durham, UK
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.,Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ, USA.,Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - David L Smith
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA. .,Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
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30
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Battle KE, Lucas TCD, Nguyen M, Howes RE, Nandi AK, Twohig KA, Pfeffer DA, Cameron E, Rao PC, Casey D, Gibson HS, Rozier JA, Dalrymple U, Keddie SH, Collins EL, Harris JR, Guerra CA, Thorn MP, Bisanzio D, Fullman N, Huynh CK, Kulikoff X, Kutz MJ, Lopez AD, Mokdad AH, Naghavi M, Nguyen G, Shackelford KA, Vos T, Wang H, Lim SS, Murray CJL, Price RN, Baird JK, Smith DL, Bhatt S, Weiss DJ, Hay SI, Gething PW. Mapping the global endemicity and clinical burden of Plasmodium vivax, 2000-17: a spatial and temporal modelling study. Lancet 2019; 394:332-343. [PMID: 31229233 PMCID: PMC6675736 DOI: 10.1016/s0140-6736(19)31096-7] [Citation(s) in RCA: 221] [Impact Index Per Article: 44.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 04/12/2019] [Accepted: 04/24/2019] [Indexed: 01/12/2023]
Abstract
BACKGROUND Plasmodium vivax exacts a significant toll on health worldwide, yet few efforts to date have quantified the extent and temporal trends of its global distribution. Given the challenges associated with the proper diagnosis and treatment of P vivax, national malaria programmes-particularly those pursuing malaria elimination strategies-require up to date assessments of P vivax endemicity and disease impact. This study presents the first global maps of P vivax clinical burden from 2000 to 2017. METHODS In this spatial and temporal modelling study, we adjusted routine malariometric surveillance data for known biases and used socioeconomic indicators to generate time series of the clinical burden of P vivax. These data informed Bayesian geospatial models, which produced fine-scale predictions of P vivax clinical incidence and infection prevalence over time. Within sub-Saharan Africa, where routine surveillance for P vivax is not standard practice, we combined predicted surfaces of Plasmodium falciparum with country-specific ratios of P vivax to P falciparum. These results were combined with surveillance-based outputs outside of Africa to generate global maps. FINDINGS We present the first high-resolution maps of P vivax burden. These results are combined with those for P falciparum (published separately) to form the malaria estimates for the Global Burden of Disease 2017 study. The burden of P vivax malaria decreased by 41·6%, from 24·5 million cases (95% uncertainty interval 22·5-27·0) in 2000 to 14·3 million cases (13·7-15·0) in 2017. The Americas had a reduction of 56·8% (47·6-67·0) in total cases since 2000, while South-East Asia recorded declines of 50·5% (50·3-50·6) and the Western Pacific regions recorded declines of 51·3% (48·0-55·4). Europe achieved zero P vivax cases during the study period. Nonetheless, rates of decline have stalled in the past five years for many countries, with particular increases noted in regions affected by political and economic instability. INTERPRETATION Our study highlights important spatial and temporal patterns in the clinical burden and prevalence of P vivax. Amid substantial progress worldwide, plateauing gains and areas of increased burden signal the potential for challenges that are greater than expected on the road to malaria elimination. These results support global monitoring systems and can inform the optimisation of diagnosis and treatment where P vivax has most impact. FUNDING Bill & Melinda Gates Foundation and the Wellcome Trust.
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Affiliation(s)
- Katherine E Battle
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Tim C D Lucas
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Michele Nguyen
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Rosalind E Howes
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Anita K Nandi
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Katherine A Twohig
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Daniel A Pfeffer
- Menzies School of Health Research and Charles Darwin University, Darwin, NT, Australia
| | - Ewan Cameron
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Puja C Rao
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Daniel Casey
- Seattle & King County Public Health, Seattle, WA, USA
| | - Harry S Gibson
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Jennifer A Rozier
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | | | - Suzanne H Keddie
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Emma L Collins
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Joseph R Harris
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Carlos A Guerra
- Medical Care Development International, Silver Spring, MD, USA
| | - Michael P Thorn
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Donal Bisanzio
- Global Health Division, RTI International, Washington, DC, USA; Epidemiology and Public Health Division, School of Medicine, University of Nottingham, Nottingham, UK
| | - Nancy Fullman
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Chantal K Huynh
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Xie Kulikoff
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Michael J Kutz
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Alan D Lopez
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Ali H Mokdad
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Mohsen Naghavi
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Grant Nguyen
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | | | - Theo Vos
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Haidong Wang
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Stephen S Lim
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | | | - Ric N Price
- Menzies School of Health Research and Charles Darwin University, Darwin, NT, Australia; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - J Kevin Baird
- Eijkman-Oxford Clinical Rearch Unit, Jakarta, Indonesia; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - David L Smith
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | | | - Daniel J Weiss
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Simon I Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Peter W Gething
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
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31
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Weiss DJ, Lucas TCD, Nguyen M, Nandi AK, Bisanzio D, Battle KE, Cameron E, Twohig KA, Pfeffer DA, Rozier JA, Gibson HS, Rao PC, Casey D, Bertozzi-Villa A, Collins EL, Dalrymple U, Gray N, Harris JR, Howes RE, Kang SY, Keddie SH, May D, Rumisha S, Thorn MP, Barber R, Fullman N, Huynh CK, Kulikoff X, Kutz MJ, Lopez AD, Mokdad AH, Naghavi M, Nguyen G, Shackelford KA, Vos T, Wang H, Smith DL, Lim SS, Murray CJL, Bhatt S, Hay SI, Gething PW. Mapping the global prevalence, incidence, and mortality of Plasmodium falciparum, 2000-17: a spatial and temporal modelling study. Lancet 2019; 394:322-331. [PMID: 31229234 PMCID: PMC6675740 DOI: 10.1016/s0140-6736(19)31097-9] [Citation(s) in RCA: 216] [Impact Index Per Article: 43.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 04/12/2019] [Accepted: 04/24/2019] [Indexed: 01/26/2023]
Abstract
BACKGROUND Since 2000, the scale-up of malaria control interventions has substantially reduced morbidity and mortality caused by the disease globally, fuelling bold aims for disease elimination. In tandem with increased availability of geospatially resolved data, malaria control programmes increasingly use high-resolution maps to characterise spatially heterogeneous patterns of disease risk and thus efficiently target areas of high burden. METHODS We updated and refined the Plasmodium falciparum parasite rate and clinical incidence models for sub-Saharan Africa, which rely on cross-sectional survey data for parasite rate and intervention coverage. For malaria endemic countries outside of sub-Saharan Africa, we produced estimates of parasite rate and incidence by applying an ecological downscaling approach to malaria incidence data acquired via routine surveillance. Mortality estimates were derived by linking incidence to systematically derived vital registration and verbal autopsy data. Informed by high-resolution covariate surfaces, we estimated P falciparum parasite rate, clinical incidence, and mortality at national, subnational, and 5 × 5 km pixel scales with corresponding uncertainty metrics. FINDINGS We present the first global, high-resolution map of P falciparum malaria mortality and the first global prevalence and incidence maps since 2010. These results are combined with those for Plasmodium vivax (published separately) to form the malaria estimates for the Global Burden of Disease 2017 study. The P falciparum estimates span the period 2000-17, and illustrate the rapid decline in burden between 2005 and 2017, with incidence declining by 27·9% and mortality declining by 42·5%. Despite a growing population in endemic regions, P falciparum cases declined between 2005 and 2017, from 232·3 million (95% uncertainty interval 198·8-277·7) to 193·9 million (156·6-240·2) and deaths declined from 925 800 (596 900-1 341 100) to 618 700 (368 600-952 200). Despite the declines in burden, 90·1% of people within sub-Saharan Africa continue to reside in endemic areas, and this region accounted for 79·4% of cases and 87·6% of deaths in 2017. INTERPRETATION High-resolution maps of P falciparum provide a contemporary resource for informing global policy and malaria control planning, programme implementation, and monitoring initiatives. Amid progress in reducing global malaria burden, areas where incidence trends have plateaued or increased in the past 5 years underscore the fragility of hard-won gains against malaria. Efforts towards elimination should be strengthened in such areas, and those where burden remained high throughout the study period. FUNDING Bill & Melinda Gates Foundation.
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Affiliation(s)
- Daniel J Weiss
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Tim C D Lucas
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Michele Nguyen
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Anita K Nandi
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Donal Bisanzio
- Global Health Division, Research Triangle Institute International, Washington, DC, USA; Public Health Division, School of Medicine, University of Nottingham, Nottingham, UK
| | - Katherine E Battle
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Ewan Cameron
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Katherine A Twohig
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Daniel A Pfeffer
- Menzies School of Health Research, Charles Darwin University, Casuarina, NT, Australia
| | - Jennifer A Rozier
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Harry S Gibson
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Puja C Rao
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Daniel Casey
- Seattle and King County Public Health, Seattle, WA, USA
| | | | - Emma L Collins
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Ursula Dalrymple
- Public Health England, Department of Health and Social Care, London, UK
| | - Naomi Gray
- Instruct: An Integrated Structural Biology Infrastructure for Europe, Oxford, UK
| | - Joseph R Harris
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Rosalind E Howes
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Sun Yun Kang
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Suzanne H Keddie
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Daniel May
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Susan Rumisha
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Michael P Thorn
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Ryan Barber
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Nancy Fullman
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Chantal K Huynh
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Xie Kulikoff
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Michael J Kutz
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Alan D Lopez
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Ali H Mokdad
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Mohsen Naghavi
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Grant Nguyen
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | | | - Theo Vos
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Haidong Wang
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - David L Smith
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Stephen S Lim
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | | | | | - Simon I Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Peter W Gething
- Malaria Atlas Project, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
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32
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Zhu SJ, Hendry JA, Almagro-Garcia J, Pearson RD, Amato R, Miles A, Weiss DJ, Lucas TC, Nguyen M, Gething PW, Kwiatkowski D, McVean G. The origins and relatedness structure of mixed infections vary with local prevalence of P. falciparum malaria. eLife 2019; 8:e40845. [PMID: 31298657 PMCID: PMC6684230 DOI: 10.7554/elife.40845] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Accepted: 07/10/2019] [Indexed: 02/07/2023] Open
Abstract
Individual malaria infections can carry multiple strains of Plasmodium falciparum with varying levels of relatedness. Yet, how local epidemiology affects the properties of such mixed infections remains unclear. Here, we develop an enhanced method for strain deconvolution from genome sequencing data, which estimates the number of strains, their proportions, identity-by-descent (IBD) profiles and individual haplotypes. Applying it to the Pf3k data set, we find that the rate of mixed infection varies from 29% to 63% across countries and that 51% of mixed infections involve more than two strains. Furthermore, we estimate that 47% of symptomatic dual infections contain sibling strains likely to have been co-transmitted from a single mosquito, and find evidence of mixed infections propagated over successive infection cycles. Finally, leveraging data from the Malaria Atlas Project, we find that prevalence correlates within Africa, but not Asia, with both the rate of mixed infection and the level of IBD.
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Affiliation(s)
- Sha Joe Zhu
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Jason A Hendry
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Jacob Almagro-Garcia
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Medical Research Council Centre for Genomics and Global Health, University of Oxford, Oxford, United Kingdom
- Wellcome Sanger Institute, Hinxton, United Kingdom
| | - Richard D Pearson
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Medical Research Council Centre for Genomics and Global Health, University of Oxford, Oxford, United Kingdom
- Wellcome Sanger Institute, Hinxton, United Kingdom
| | - Roberto Amato
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Medical Research Council Centre for Genomics and Global Health, University of Oxford, Oxford, United Kingdom
- Wellcome Sanger Institute, Hinxton, United Kingdom
| | - Alistair Miles
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Medical Research Council Centre for Genomics and Global Health, University of Oxford, Oxford, United Kingdom
- Wellcome Sanger Institute, Hinxton, United Kingdom
| | - Daniel J Weiss
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Tim Cd Lucas
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Michele Nguyen
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Peter W Gething
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Dominic Kwiatkowski
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Medical Research Council Centre for Genomics and Global Health, University of Oxford, Oxford, United Kingdom
- Wellcome Sanger Institute, Hinxton, United Kingdom
| | - Gil McVean
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
- Medical Research Council Centre for Genomics and Global Health, University of Oxford, Oxford, United Kingdom
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33
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Dalrymple U, Cameron E, Arambepola R, Battle KE, Chestnutt EG, Keddie SH, Twohig KA, Pfeffer DA, Gibson HS, Weiss DJ, Bhatt S, Gething PW. The contribution of non-malarial febrile illness co-infections to Plasmodium falciparum case counts in health facilities in sub-Saharan Africa. Malar J 2019; 18:195. [PMID: 31186004 PMCID: PMC6560910 DOI: 10.1186/s12936-019-2830-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Accepted: 06/04/2019] [Indexed: 01/20/2023] Open
Abstract
Background The disease burden of Plasmodium falciparum malaria illness is generally estimated using one of two distinct approaches: either by transforming P. falciparum infection prevalence estimates into incidence estimates using conversion formulae; or through adjustment of counts of recorded P. falciparum-positive fever cases from clinics. Whilst both ostensibly seek to evaluate P. falciparum disease burden, there is an implicit and problematic difference in the metric being estimated. The first enumerates only symptomatic malaria cases, while the second enumerates all febrile episodes coincident with a P. falciparum infection, regardless of the fever’s underlying cause. Methods Here, a novel approach was used to triangulate community-based data sources capturing P. falciparum infection, fever, and care-seeking to estimate the fraction of P. falciparum-positive fevers amongst children under 5 years of age presenting at health facilities that are attributable to P. falciparum infection versus other non-malarial causes. A Bayesian hierarchical model was used to assign probabilities of malaria-attributable fever (MAF) and non-malarial febrile illness (NMFI) to children under five from a dataset of 41 surveys from 21 countries in sub-Saharan Africa conducted between 2006 and 2016. Using subsequent treatment-seeking outcomes, the proportion of MAF and NMFI amongst P. falciparum-positive febrile children presenting at public clinics was estimated. Results Across all surveyed malaria-positive febrile children who sought care at public clinics across 41 country-years in sub-Saharan Africa, P. falciparum infection was estimated to be the underlying cause of only 37.7% (31.1–45.4, 95% CrI) of P. falciparum-positive fevers, with significant geographical and temporal heterogeneity between surveys. Conclusions These findings highlight the complex nature of the P. falciparum burden amongst children under 5 years of age and indicate that for many children presenting at health clinics, a positive P. falciparum diagnosis and a fever does not necessarily mean P. falciparum is the underlying cause of the child’s symptoms, and thus other causes of illness should always be investigated, in addition to prescribing an effective anti-malarial medication. In addition to providing new large-scale estimates of malaria-attributable fever prevalence, the results presented here improve comparability between different methods for calculating P. falciparum disease burden, with significant implications for national and global estimation of malaria burden. Electronic supplementary material The online version of this article (10.1186/s12936-019-2830-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ursula Dalrymple
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, OX3 7LF, UK.,Department of Zoology, University of Oxford, Zoology Research and Administration Building, 11a Mansfield Road, Oxford, OX1 3SZ, UK
| | - Ewan Cameron
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, OX3 7LF, UK
| | - Rohan Arambepola
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, OX3 7LF, UK
| | - Katherine E Battle
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, OX3 7LF, UK
| | - Elisabeth G Chestnutt
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, OX3 7LF, UK
| | - Suzanne H Keddie
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, OX3 7LF, UK
| | - Katherine A Twohig
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, OX3 7LF, UK
| | - Daniel A Pfeffer
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, OX3 7LF, UK.,Global and Tropical Health Division, Menzies School of Health Research and Charles Darwin University, Darwin, Australia
| | - Harry S Gibson
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, OX3 7LF, UK
| | - Daniel J Weiss
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, OX3 7LF, UK
| | - Samir Bhatt
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, OX3 7LF, UK.,School of Public Health, Faculty of Medicine, Imperial College London, St Mary's Hospital, Norfolk Place, London, W2 1PG, UK
| | - Peter W Gething
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, OX3 7LF, UK.
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Mosser JF, Gagne-Maynard W, Rao PC, Osgood-Zimmerman A, Fullman N, Graetz N, Burstein R, Updike RL, Liu PY, Ray SE, Earl L, Deshpande A, Casey DC, Dwyer-Lindgren L, Cromwell EA, Pigott DM, Shearer FM, Larson HJ, Weiss DJ, Bhatt S, Gething PW, Murray CJL, Lim SS, Reiner RC, Hay SI. Mapping diphtheria-pertussis-tetanus vaccine coverage in Africa, 2000-2016: a spatial and temporal modelling study. Lancet 2019; 393:1843-1855. [PMID: 30961907 PMCID: PMC6497987 DOI: 10.1016/s0140-6736(19)30226-0] [Citation(s) in RCA: 80] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2018] [Revised: 12/19/2018] [Accepted: 01/15/2019] [Indexed: 01/29/2023]
Abstract
BACKGROUND Routine childhood vaccination is among the most cost-effective, successful public health interventions available. Amid substantial investments to expand vaccine delivery throughout Africa and strengthen administrative reporting systems, most countries still require robust measures of local routine vaccine coverage and changes in geographical inequalities over time. METHODS This analysis drew from 183 surveys done between 2000 and 2016, including data from 881 268 children in 49 African countries. We used a Bayesian geostatistical model calibrated to results from the Global Burden of Diseases, Injuries, and Risk Factors Study 2017, to produce annual estimates with high-spatial resolution (5 × 5 km) of diphtheria-pertussis-tetanus (DPT) vaccine coverage and dropout for children aged 12-23 months in 52 African countries from 2000 to 2016. FINDINGS Estimated third-dose (DPT3) coverage increased in 72·3% (95% uncertainty interval [UI] 64·6-80·3) of second-level administrative units in Africa from 2000 to 2016, but substantial geographical inequalities in DPT coverage remained across and within African countries. In 2016, DPT3 coverage at the second administrative (ie, district) level varied by more than 25% in 29 of 52 countries, with only two (Morocco and Rwanda) of 52 countries meeting the Global Vaccine Action Plan target of 80% DPT3 coverage or higher in all second-level administrative units with high confidence (posterior probability ≥95%). Large areas of low DPT3 coverage (≤50%) were identified in the Sahel, Somalia, eastern Ethiopia, and in Angola. Low first-dose (DPT1) coverage (≤50%) and high relative dropout (≥30%) together drove low DPT3 coverage across the Sahel, Somalia, eastern Ethiopia, Guinea, and Angola. INTERPRETATION Despite substantial progress in Africa, marked national and subnational inequalities in DPT coverage persist throughout the continent. These results can help identify areas of low coverage and vaccine delivery system vulnerabilities and can ultimately support more precise targeting of resources to improve vaccine coverage and health outcomes for African children. FUNDING Bill & Melinda Gates Foundation.
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Affiliation(s)
- Jonathan F Mosser
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - William Gagne-Maynard
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Puja C Rao
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | | | - Nancy Fullman
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Nicholas Graetz
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Roy Burstein
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Rachel L Updike
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Patrick Y Liu
- David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Sarah E Ray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Lucas Earl
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Aniruddha Deshpande
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Daniel C Casey
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Laura Dwyer-Lindgren
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Elizabeth A Cromwell
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA
| | - David M Pigott
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA
| | | | - Heidi Jane Larson
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | | | - Samir Bhatt
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | | | - Christopher J L Murray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA
| | - Stephen S Lim
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA.
| | - Robert C Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA
| | - Simon I Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA.
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Tusting LS, Bisanzio D, Alabaster G, Cameron E, Cibulskis R, Davies M, Flaxman S, Gibson HS, Knudsen J, Mbogo C, Okumu FO, von Seidlein L, Weiss DJ, Lindsay SW, Gething PW, Bhatt S. Mapping changes in housing in sub-Saharan Africa from 2000 to 2015. Nature 2019; 568:391-394. [PMID: 30918405 PMCID: PMC6784864 DOI: 10.1038/s41586-019-1050-5] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Accepted: 02/20/2019] [Indexed: 11/16/2022]
Abstract
Access to adequate housing is a fundamental human right, essential to human security, nutrition and health, and a core objective of the United Nations Sustainable Development Goals1,2. Globally, the housing need is most acute in Africa, where the population will more than double by 2050. However, existing data on housing quality across Africa are limited primarily to urban areas and are mostly recorded at the national level. Here we quantify changes in housing in sub-Saharan Africa from 2000 to 2015 by combining national survey data within a geostatistical framework. We show a marked transformation of housing in urban and rural sub-Saharan Africa between 2000 and 2015, with the prevalence of improved housing (with improved water and sanitation, sufficient living area and durable construction) doubling from 11% (95% confidence interval, 10-12%) to 23% (21-25%). However, 53 (50-57) million urban Africans (47% (44-50%) of the urban population analysed) were living in unimproved housing in 2015. We provide high-resolution, standardized estimates of housing conditions across sub-Saharan Africa. Our maps provide a baseline for measuring change and a mechanism to guide interventions during the era of the Sustainable Development Goals.
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Affiliation(s)
- Lucy S Tusting
- Department of Disease Control, London School of Hygiene & Tropical Medicine, London, UK.
| | - Donal Bisanzio
- RTI International, Washington, DC, USA
- Division of Epidemiology and Public Health, School of Medicine, University of Nottingham, Nottingham, UK
| | | | - Ewan Cameron
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Richard Cibulskis
- Health Metrics and Measurement Cluster, World Health Organization, Geneva, Switzerland
| | - Michael Davies
- UCL Institute for Environmental Design and Engineering (IEDE), University College London, London, UK
| | - Seth Flaxman
- Department of Mathematics and Data Science Institute, Imperial College London, London, UK
| | - Harry S Gibson
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Jakob Knudsen
- School of Architecture, The Royal Danish Academy of Fine Arts, Copenhagen, Denmark
| | - Charles Mbogo
- Kenya Medical Research Institute, Kilifi, Kenya
- KEMRI-Wellcome Trust Research Program, Nairobi, Kenya
| | - Fredros O Okumu
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Ifakara, Tanzania
- School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK
| | - Lorenz von Seidlein
- Mahidol-Oxford Tropical Medicine Research Unit (MORU), Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Daniel J Weiss
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | | | - Peter W Gething
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Samir Bhatt
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Disease Epidemiology, Imperial College London, London, UK
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36
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Howes RE, Hawa K, Andriamamonjy VF, Franchard T, Miarimbola R, Mioramalala SA, Rafamatanantsoa JF, Rahantamalala MAM, Rajaobary SH, Rajaonera HDG, Rakotonindrainy AP, Rakotoson Andrianjatonavalona C, Randriamiarinjatovo DNAL, Randrianasolo FM, Ramasy Razafindratovo RM, Ravaoarimanga M, Ye M, Gething PW, Taylor CA. A stakeholder workshop about modelled maps of key malaria indicator survey indicators in Madagascar. Malar J 2019; 18:90. [PMID: 30902070 PMCID: PMC6431047 DOI: 10.1186/s12936-019-2729-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Accepted: 03/14/2019] [Indexed: 11/29/2022] Open
Abstract
The Demographic and Health Surveys (DHS) Program has supported three household Malaria Indicator Surveys (MIS) in Madagascar. The results of 13 key malaria indicators from these surveys have been mapped as continuous surfaces using model-based geostatistical methods. The opportunities and limitations of these mapped outputs were discussed during a workshop in Antananarivo, Madagascar in July 2018, attended by 15 representatives from various implementation, policy and research stakeholder institutions in Madagascar. Participants evaluated the findings from the maps, using these to develop figures and narratives to support their work in the control of malaria in Madagascar.
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Affiliation(s)
- Rosalind E Howes
- Malaria Atlas Project, Nuffield Department of Medicine, Big Data Institute, University of Oxford, Oxford, UK.
| | - Kaleem Hawa
- Malaria Atlas Project, Nuffield Department of Medicine, Big Data Institute, University of Oxford, Oxford, UK
| | | | - Thierry Franchard
- Ministry of Health, Antananarivo, Madagascar.,Faculty of Science, University of Antananarivo, Antananarivo, Madagascar
| | - Raharizo Miarimbola
- Ministry of Health, Antananarivo, Madagascar.,Department of Public Health, Faculty of Medicine, University of Antananarivo, Antananarivo, Madagascar
| | - Sedera Aurélien Mioramalala
- Ministry of Health, Antananarivo, Madagascar.,Department of Public Health, Faculty of Medicine, University of Antananarivo, Antananarivo, Madagascar
| | | | - Mirana Ando Mbolatiana Rahantamalala
- National Malaria Control Programme, Ministry of Health, Antananarivo, Madagascar.,Department of Public Health, Faculty of Medicine, University of Antananarivo, Antananarivo, Madagascar
| | | | | | | | | | | | | | | | | | - Maurice Ye
- MEASURE-Evaluation, ICF, Antananarivo, Madagascar
| | - Peter W Gething
- Malaria Atlas Project, Nuffield Department of Medicine, Big Data Institute, University of Oxford, Oxford, UK
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Twohig KA, Pfeffer DA, Baird JK, Price RN, Zimmerman PA, Hay SI, Gething PW, Battle KE, Howes RE. Growing evidence of Plasmodium vivax across malaria-endemic Africa. PLoS Negl Trop Dis 2019; 13:e0007140. [PMID: 30703083 PMCID: PMC6372205 DOI: 10.1371/journal.pntd.0007140] [Citation(s) in RCA: 102] [Impact Index Per Article: 20.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 02/12/2019] [Accepted: 01/07/2019] [Indexed: 01/12/2023] Open
Abstract
Effective malaria control strategies require an accurate understanding of the epidemiology of locally transmitted Plasmodium species. Compared to Plasmodium falciparum infection, Plasmodium vivax has a lower asexual parasitaemia, forms dormant liver-stages (hypnozoites), and is more transmissible. Hence, treatment and diagnostic policies aimed exclusively at P. falciparum are far less efficient against endemic P. vivax. Within sub-Saharan Africa, malaria control programmes justly focus on reducing the morbidity and mortality associated with P. falciparum. However, the recent emphasis on malaria elimination and increased accessibility of more sensitive diagnostic tools have revealed greater intricacies in malaria epidemiology across the continent. Since 2010, the number of studies identifying P. vivax endemic to Africa has expanded considerably, with 88 new scientific reports published since a review of evidence in 2015, approximately doubling the available data. There is evidence of P. vivax in all regions of Africa, apparent from infected vectors, clinical cases, serological indicators, parasite prevalence, exported infections, and P. vivax-infected Duffy-negative individuals. Where the prevalence of microscopic parasitaemia is low, a greater proportion of P. vivax infections were observed relative to P. falciparum. This evidence highlights an underlying widespread presence of P. vivax across all malaria-endemic regions of Africa, further complicating the current practical understanding of malaria epidemiology in this region. Thus, ultimate elimination of malaria in Africa will require national malaria control programmes to adopt policy and practice aimed at all human species of malaria.
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Affiliation(s)
- Katherine A. Twohig
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom,* E-mail: (KAT); (REH)
| | - Daniel A. Pfeffer
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom,Global and Tropical Health Division, Menzies School of Health Research and Charles Darwin University, Darwin, Australia
| | - J. Kevin Baird
- Eijkman-Oxford Clinical Research Unit, Eijkman Institute of Molecular Biology, Jakarta, Indonesia,Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Ric N. Price
- Global and Tropical Health Division, Menzies School of Health Research and Charles Darwin University, Darwin, Australia,Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Peter A. Zimmerman
- The Center for Global Health & Diseases, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Simon I. Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America
| | - Peter W. Gething
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Katherine E. Battle
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Rosalind E. Howes
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom,* E-mail: (KAT); (REH)
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Dicker D, Nguyen G, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, Abbastabar H, Abd-Allah F, Abdela J, Abdelalim A, Abdel-Rahman O, Abdi A, Abdollahpour I, Abdulkader RS, Abdurahman AA, Abebe HT, Abebe M, Abebe Z, Abebo TA, Aboyans V, Abraha HN, Abrham AR, Abu-Raddad LJ, Abu-Rmeileh NME, Accrombessi MMK, Acharya P, Adebayo OM, Adedeji IA, Adedoyin RA, Adekanmbi V, Adetokunboh OO, Adhena BM, Adhikari TB, Adib MG, Adou AK, Adsuar JC, Afarideh M, Afshin A, Agarwal G, Aggarwal R, Aghayan SA, Agrawal S, Agrawal A, Ahmadi M, Ahmadi A, Ahmadieh H, Ahmed MLCB, Ahmed S, Ahmed MB, Aichour AN, Aichour I, Aichour MTE, Akanda AS, Akbari ME, Akibu M, Akinyemi RO, Akinyemiju T, Akseer N, Alahdab F, Al-Aly Z, Alam K, Alebel A, Aleman AV, Alene KA, Al-Eyadhy A, Ali R, Alijanzadeh M, Alizadeh-Navaei R, Aljunid SM, Alkerwi A, Alla F, Allebeck P, Allen CA, Alonso J, Al-Raddadi RM, Alsharif U, Altirkawi K, Alvis-Guzman N, Amare AT, Amini E, Ammar W, Amoako YA, Anber NH, Andrei CL, Androudi S, Animut MD, Anjomshoa M, Anlay DZ, Ansari H, Ansariadi A, Ansha MG, Antonio CAT, Appiah SCY, Aremu O, Areri HA, Ärnlöv J, Arora M, Artaman A, Aryal KK, Asadi-Lari M, Asayesh H, Asfaw ET, Asgedom SW, Assadi R, Ataro Z, Atey TMM, Athari SS, Atique S, Atre SR, Atteraya MS, Attia EF, Ausloos M, Avila-Burgos L, Avokpaho EFGA, Awasthi A, Awuah B, Ayala Quintanilla BP, Ayele HT, Ayele Y, Ayer R, Ayuk TB, Azzopardi PS, Azzopardi-Muscat N, Badali H, Badawi A, Balakrishnan K, Bali AG, Banach M, Banstola A, Barac A, Barboza MA, Barquera S, Barrero LH, Basaleem H, Bassat Q, Basu A, Basu S, Baune BT, Bazargan-Hejazi S, Bedi N, Beghi E, Behzadifar M, Behzadifar M, Béjot Y, Bekele BB, Belachew AB, Belay AG, Belay E, Belay SA, Belay YA, Bell ML, Bello AK, Bennett DA, Bensenor IM, Berhane A, Berman AE, Bernabe E, Bernstein RS, Bertolacci GJ, Beuran M, Beyranvand T, Bhala N, Bhatia E, Bhatt S, Bhattarai S, Bhaumik S, Bhutta ZA, Biadgo B, Bijani A, Bikbov B, Bililign N, Bin Sayeed MS, Birlik SM, Birungi C, Bisanzio D, Biswas T, Bjørge T, Bleyer A, Basara BB, Bose D, Bosetti C, Boufous S, Bourne R, Brady OJ, Bragazzi NL, Brant LC, Brazinova A, Breitborde NJK, Brenner H, Britton G, Brugha T, Burke KE, Busse R, Butt ZA, Cahuana-Hurtado L, Callender CSKH, Campos-Nonato IR, Campuzano Rincon JC, Cano J, Car M, Cárdenas R, Carreras G, Carrero JJ, Carter A, Carvalho F, Castañeda-Orjuela CA, Castillo Rivas J, Castro F, Catalá-López F, Çavlin A, Cerin E, Chaiah Y, Champs AP, Chang HY, Chang JC, Chattopadhyay A, Chaturvedi P, Chen W, Chiang PPC, Chimed-Ochir O, Chin KL, Chisumpa VH, Chitheer A, Choi JYJ, Christensen H, Christopher DJ, Chung SC, Cicuttini FM, Ciobanu LG, Cirillo M, Claro RM, Cohen AJ, Collado-Mateo D, Constantin MM, Conti S, Cooper C, Cooper LT, Cortesi PA, Cortinovis M, Cousin E, Criqui MH, Cromwell EA, Crowe CS, Crump JA, Cucu A, Cunningham M, Daba AK, Dachew BA, Dadi AF, Dandona L, Dandona R, Dang AK, Dargan PI, Daryani A, Das SK, Das Gupta R, das Neves J, Dasa TT, Dash AP, Weaver ND, Davitoiu DV, Davletov K, Dayama A, Courten BD, De la Hoz FP, De leo D, De Neve JW, Degefa MG, Degenhardt L, Degfie TT, Deiparine S, Dellavalle RP, Demoz GT, Demtsu BB, Denova-Gutiérrez E, Deribe K, Dervenis N, Des Jarlais DC, Dessie GA, Dey S, Dharmaratne SD, Dhimal M, Ding EL, Djalalinia S, Doku DT, Dolan KA, Donnelly CA, Dorsey ER, Douwes-Schultz D, Doyle KE, Drake TM, Driscoll TR, Dubey M, Dubljanin E, Duken EE, Duncan BB, Duraes AR, Ebrahimi H, Ebrahimpour S, Edessa D, Edvardsson D, Eggen AE, El Bcheraoui C, El Sayed Zaki M, Elfaramawi M, El-Khatib Z, Ellingsen CL, Elyazar IRF, Enayati A, Endries AYY, Er B, Ermakov SP, Eshrati B, Eskandarieh S, Esmaeili R, Esteghamati A, Esteghamati S, Fakhar M, Fakhim H, Farag T, Faramarzi M, Fareed M, Farhadi F, Farid TA, Farinha CSES, Farioli A, Faro A, Farvid MS, Farzadfar F, Farzaei MH, Fazeli MS, Feigin VL, Feigl AB, Feizy F, Fentahun N, Fereshtehnejad SM, Fernandes E, Fernandes JC, Feyissa GT, Fijabi DO, Filip I, Finegold S, Fischer F, Flor LS, Foigt NA, Ford JA, Foreman KJ, Fornari C, Frank TD, Franklin RC, Fukumoto T, Fuller JE, Fullman N, Fürst T, Furtado JM, Futran ND, Galan A, Gallus S, Gambashidze K, Gamkrelidze A, Gankpe FG, Garcia-Basteiro AL, Garcia-Gordillo MA, Gebre T, Gebre AK, Gebregergs GB, Gebrehiwot TT, Gebremedhin AT, Gelano TF, Gelaw YA, Geleijnse JM, Genova-Maleras R, Gessner BD, Getachew S, Gething PW, Gezae KE, Ghadami MR, Ghadimi R, Ghasemi Falavarjani K, Ghasemi-Kasman M, Ghiasvand H, Ghimire M, Ghoshal AG, Gill PS, Gill TK, Gillum RF, Giussani G, Goenka S, Goli S, Gomez RS, Gomez-Cabrera MC, Gómez-Dantés H, Gona PN, Goodridge A, Gopalani SV, Goto A, Goulart AC, Goulart BNG, Grada A, Grosso G, Gugnani HC, Guimaraes ALS, Guo Y, Gupta PC, Gupta R, Gupta R, Gupta T, Gyawali B, Haagsma JA, Hachinski V, Hafezi-Nejad N, Hagos TB, Hailegiyorgis TT, Hailu GB, Haj-Mirzaian A, Haj-Mirzaian A, Hamadeh RR, Hamidi S, Handal AJ, Hankey GJ, Harb HL, Harikrishnan S, Haririan H, Haro JM, Hasan M, Hassankhani H, Hassen HY, Havmoeller R, Hay RJ, Hay SI, He Y, Hedayatizadeh-Omran A, Hegazy MI, Heibati B, Heidari M, Hendrie D, Henok A, Henry NJ, Heredia-Pi I, Herteliu C, Heydarpour F, Heydarpour P, Heydarpour S, Hibstu DT, Hoek HW, Hole MK, Homaie Rad E, Hoogar P, Horino M, Hosgood HD, Hosseini SM, Hosseinzadeh M, Hostiuc S, Hostiuc M, Hotez PJ, Hoy DG, Hsairi M, Htet AS, Hu G, Huang JJ, Husseini A, Hussen MM, Hutfless S, Iburg KM, Igumbor EU, Ikeda CT, Ilesanmi OS, Iqbal U, Irvani SSN, Isehunwa OO, Islam SMS, Islami F, Jahangiry L, Jahanmehr N, Jain R, Jain SK, Jakovljevic M, James SL, Javanbakht M, Jayaraman S, Jayatilleke AU, Jee SH, Jeemon P, Jha RP, Jha V, Ji JS, Johnson SC, Jonas JB, Joshi A, Jozwiak JJ, Jungari SB, Jürisson M, K M, Kabir Z, Kadel R, Kahsay A, Kahssay M, Kalani R, Kapil U, Karami M, Karami Matin B, Karch A, Karema C, Karimi N, Karimi SM, Karimi-Sari H, Kasaeian A, Kassa GM, Kassa TD, Kassa ZY, Kassebaum NJ, Katibeh M, Katikireddi SV, Kaul A, Kawakami N, Kazemeini H, Kazemi Z, Karyani AK, K C P, Kebede S, Keiyoro PN, Kemp GR, Kengne AP, Keren A, Kereselidze M, Khader YS, Khafaie MA, Khajavi A, Khalid N, Khalil IA, Khan EA, Khan G, Khan MS, Khan MA, Khang YH, Khanna T, Khater MM, Khatony A, Khazaie H, Khoja AT, Khosravi A, Khosravi MH, Khubchandani J, Kiadaliri AA, Kibret GDD, Kim CI, Kim D, Kim JY, Kim YE, Kimokoti RW, Kinfu Y, Kinra S, Kisa A, Kissimova-Skarbek K, Kissoon N, Kivimäki M, Kleber ME, Knibbs LD, Knudsen AKS, Kochhar S, Kokubo Y, Kolola T, Kopec JA, Kosek MN, Kosen S, Koul PA, Koyanagi A, Kravchenko MA, Krishan K, Krishnaswami S, Kuate Defo B, Kucuk Bicer B, Kudom AA, Kuipers EJ, Kulikoff XR, Kumar GA, Kumar M, Kumar P, Kumsa FA, Kutz MJ, Lad SD, Lafranconi A, Lal DK, Lalloo R, Lam H, Lami FH, Lan Q, Langan SM, Lansingh VC, Lansky S, Larson HJ, Laryea DO, Lassi ZS, Latifi A, Lavados PM, Laxmaiah A, Lazarus JV, Lebedev G, Lee PH, Leigh J, Leshargie CT, Leta S, Levi M, Li S, Li Y, Li X, Liang J, Liang X, Liben ML, Lim LL, Lim SS, Limenih MA, Linn S, Liu S, Liu Y, Lodha R, Logroscino G, Lonsdale C, Lorch SA, Lorkowski S, Lotufo PA, Lozano R, Lucas TCD, Lunevicius R, Lyons RA, Ma S, Mabika C, Macarayan ERK, Mackay MT, Maddison ER, Maddison R, Madotto F, Magdy Abd El Razek H, Magdy Abd El Razek M, Maghavani DP, Majdan M, Majdzadeh R, Majeed A, Malekzadeh R, Malik MA, Malta DC, Mamun AA, Manamo WA, Manda AL, Mansournia MA, Mantovani LG, Mapoma CC, Marami D, Maravilla JC, Marcenes W, Marina S, Martinez-Raga J, Martins SCO, Martins-Melo FR, März W, Marzan MB, Mashamba-Thompson TP, Masiye F, Massenburg BB, Maulik PK, Mazidi M, McGrath JJ, McKee M, Mehata S, Mehendale SM, Mehndiratta MM, Mehrotra R, Mehta KM, Mehta V, Mekonen T, Mekonnen TC, Meles HG, Meles KG, Melese A, Melku M, Memiah PTN, Memish ZA, Mendoza W, Mengistu DT, Mengistu G, Mensah GA, Mereta ST, Meretoja A, Meretoja TJ, Mestrovic T, Mezgebe HB, Miangotar Y, Miazgowski B, Miazgowski T, Miller TR, Mini GK, Mirica A, Mirrakhimov EM, Misganaw AT, 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Yamada T, Yan LL, Yano Y, Yaseri M, Yasin YJ, Ye P, Yearwood JA, Yentür GK, Yeshaneh A, Yimer EM, Yip P, Yisma E, Yonemoto N, Yoon SJ, York HW, Yotebieng M, Younis MZ, Yousefifard M, Yu C, Zachariah G, Zadnik V, Zafar S, Zaidi Z, Zaman SB, Zamani M, Zare Z, Zeeb H, Zeleke MM, Zenebe ZM, Zerfu TA, Zhang K, Zhang X, Zhou M, Zhu J, Zodpey S, Zucker I, Zuhlke LJJ, Lopez AD, Gakidou E, Murray CJL. Global, regional, and national age-sex-specific mortality and life expectancy, 1950-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018; 392:1684-1735. [PMID: 30496102 PMCID: PMC6227504 DOI: 10.1016/s0140-6736(18)31891-9] [Citation(s) in RCA: 575] [Impact Index Per Article: 95.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Revised: 07/14/2018] [Accepted: 08/08/2018] [Indexed: 02/07/2023]
Abstract
BACKGROUND Assessments of age-specific mortality and life expectancy have been done by the UN Population Division, Department of Economics and Social Affairs (UNPOP), the United States Census Bureau, WHO, and as part of previous iterations of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD). Previous iterations of the GBD used population estimates from UNPOP, which were not derived in a way that was internally consistent with the estimates of the numbers of deaths in the GBD. The present iteration of the GBD, GBD 2017, improves on previous assessments and provides timely estimates of the mortality experience of populations globally. METHODS The GBD uses all available data to produce estimates of mortality rates between 1950 and 2017 for 23 age groups, both sexes, and 918 locations, including 195 countries and territories and subnational locations for 16 countries. Data used include vital registration systems, sample registration systems, household surveys (complete birth histories, summary birth histories, sibling histories), censuses (summary birth histories, household deaths), and Demographic Surveillance Sites. In total, this analysis used 8259 data sources. Estimates of the probability of death between birth and the age of 5 years and between ages 15 and 60 years are generated and then input into a model life table system to produce complete life tables for all locations and years. Fatal discontinuities and mortality due to HIV/AIDS are analysed separately and then incorporated into the estimation. We analyse the relationship between age-specific mortality and development status using the Socio-demographic Index, a composite measure based on fertility under the age of 25 years, education, and income. There are four main methodological improvements in GBD 2017 compared with GBD 2016: 622 additional data sources have been incorporated; new estimates of population, generated by the GBD study, are used; statistical methods used in different components of the analysis have been further standardised and improved; and the analysis has been extended backwards in time by two decades to start in 1950. FINDINGS Globally, 18·7% (95% uncertainty interval 18·4-19·0) of deaths were registered in 1950 and that proportion has been steadily increasing since, with 58·8% (58·2-59·3) of all deaths being registered in 2015. At the global level, between 1950 and 2017, life expectancy increased from 48·1 years (46·5-49·6) to 70·5 years (70·1-70·8) for men and from 52·9 years (51·7-54·0) to 75·6 years (75·3-75·9) for women. Despite this overall progress, there remains substantial variation in life expectancy at birth in 2017, which ranges from 49·1 years (46·5-51·7) for men in the Central African Republic to 87·6 years (86·9-88·1) among women in Singapore. The greatest progress across age groups was for children younger than 5 years; under-5 mortality dropped from 216·0 deaths (196·3-238·1) per 1000 livebirths in 1950 to 38·9 deaths (35·6-42·83) per 1000 livebirths in 2017, with huge reductions across countries. Nevertheless, there were still 5·4 million (5·2-5·6) deaths among children younger than 5 years in the world in 2017. Progress has been less pronounced and more variable for adults, especially for adult males, who had stagnant or increasing mortality rates in several countries. The gap between male and female life expectancy between 1950 and 2017, while relatively stable at the global level, shows distinctive patterns across super-regions and has consistently been the largest in central Europe, eastern Europe, and central Asia, and smallest in south Asia. Performance was also variable across countries and time in observed mortality rates compared with those expected on the basis of development. INTERPRETATION This analysis of age-sex-specific mortality shows that there are remarkably complex patterns in population mortality across countries. The findings of this study highlight global successes, such as the large decline in under-5 mortality, which reflects significant local, national, and global commitment and investment over several decades. However, they also bring attention to mortality patterns that are a cause for concern, particularly among adult men and, to a lesser extent, women, whose mortality rates have stagnated in many countries over the time period of this study, and in some cases are increasing. FUNDING Bill & Melinda Gates Foundation.
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Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018; 392:1736-1788. [PMID: 30496103 PMCID: PMC6227606 DOI: 10.1016/s0140-6736(18)32203-7] [Citation(s) in RCA: 4178] [Impact Index Per Article: 696.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2018] [Revised: 08/29/2018] [Accepted: 08/30/2018] [Indexed: 02/07/2023]
Abstract
BACKGROUND Global development goals increasingly rely on country-specific estimates for benchmarking a nation's progress. To meet this need, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 estimated global, regional, national, and, for selected locations, subnational cause-specific mortality beginning in the year 1980. Here we report an update to that study, making use of newly available data and improved methods. GBD 2017 provides a comprehensive assessment of cause-specific mortality for 282 causes in 195 countries and territories from 1980 to 2017. METHODS The causes of death database is composed of vital registration (VR), verbal autopsy (VA), registry, survey, police, and surveillance data. GBD 2017 added ten VA studies, 127 country-years of VR data, 502 cancer-registry country-years, and an additional surveillance country-year. Expansions of the GBD cause of death hierarchy resulted in 18 additional causes estimated for GBD 2017. Newly available data led to subnational estimates for five additional countries-Ethiopia, Iran, New Zealand, Norway, and Russia. Deaths assigned International Classification of Diseases (ICD) codes for non-specific, implausible, or intermediate causes of death were reassigned to underlying causes by redistribution algorithms that were incorporated into uncertainty estimation. We used statistical modelling tools developed for GBD, including the Cause of Death Ensemble model (CODEm), to generate cause fractions and cause-specific death rates for each location, year, age, and sex. Instead of using UN estimates as in previous versions, GBD 2017 independently estimated population size and fertility rate for all locations. Years of life lost (YLLs) were then calculated as the sum of each death multiplied by the standard life expectancy at each age. All rates reported here are age-standardised. FINDINGS At the broadest grouping of causes of death (Level 1), non-communicable diseases (NCDs) comprised the greatest fraction of deaths, contributing to 73·4% (95% uncertainty interval [UI] 72·5-74·1) of total deaths in 2017, while communicable, maternal, neonatal, and nutritional (CMNN) causes accounted for 18·6% (17·9-19·6), and injuries 8·0% (7·7-8·2). Total numbers of deaths from NCD causes increased from 2007 to 2017 by 22·7% (21·5-23·9), representing an additional 7·61 million (7·20-8·01) deaths estimated in 2017 versus 2007. The death rate from NCDs decreased globally by 7·9% (7·0-8·8). The number of deaths for CMNN causes decreased by 22·2% (20·0-24·0) and the death rate by 31·8% (30·1-33·3). Total deaths from injuries increased by 2·3% (0·5-4·0) between 2007 and 2017, and the death rate from injuries decreased by 13·7% (12·2-15·1) to 57·9 deaths (55·9-59·2) per 100 000 in 2017. Deaths from substance use disorders also increased, rising from 284 000 deaths (268 000-289 000) globally in 2007 to 352 000 (334 000-363 000) in 2017. Between 2007 and 2017, total deaths from conflict and terrorism increased by 118·0% (88·8-148·6). A greater reduction in total deaths and death rates was observed for some CMNN causes among children younger than 5 years than for older adults, such as a 36·4% (32·2-40·6) reduction in deaths from lower respiratory infections for children younger than 5 years compared with a 33·6% (31·2-36·1) increase in adults older than 70 years. Globally, the number of deaths was greater for men than for women at most ages in 2017, except at ages older than 85 years. Trends in global YLLs reflect an epidemiological transition, with decreases in total YLLs from enteric infections, respiratory infections and tuberculosis, and maternal and neonatal disorders between 1990 and 2017; these were generally greater in magnitude at the lowest levels of the Socio-demographic Index (SDI). At the same time, there were large increases in YLLs from neoplasms and cardiovascular diseases. YLL rates decreased across the five leading Level 2 causes in all SDI quintiles. The leading causes of YLLs in 1990-neonatal disorders, lower respiratory infections, and diarrhoeal diseases-were ranked second, fourth, and fifth, in 2017. Meanwhile, estimated YLLs increased for ischaemic heart disease (ranked first in 2017) and stroke (ranked third), even though YLL rates decreased. Population growth contributed to increased total deaths across the 20 leading Level 2 causes of mortality between 2007 and 2017. Decreases in the cause-specific mortality rate reduced the effect of population growth for all but three causes: substance use disorders, neurological disorders, and skin and subcutaneous diseases. INTERPRETATION Improvements in global health have been unevenly distributed among populations. Deaths due to injuries, substance use disorders, armed conflict and terrorism, neoplasms, and cardiovascular disease are expanding threats to global health. For causes of death such as lower respiratory and enteric infections, more rapid progress occurred for children than for the oldest adults, and there is continuing disparity in mortality rates by sex across age groups. Reductions in the death rate of some common diseases are themselves slowing or have ceased, primarily for NCDs, and the death rate for selected causes has increased in the past decade. FUNDING Bill & Melinda Gates Foundation.
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Santos IS, Santos JV, Santric Milicevic MM, Sao Jose BP, Sardana M, Sarker AR, Sarrafzadegan N, Sartorius B, Sarvi S, Sathian B, Satpathy M, Sawant AR, Sawhney M, Saxena S, Saylan M, Schaeffner E, Schmidt MI, Schneider IJC, Schöttker B, Schwebel DC, Schwendicke F, Scott JG, Sekerija M, Sepanlou SG, Serván-Mori E, Seyedmousavi S, Shabaninejad H, Shafieesabet A, Shahbazi M, Shaheen AA, Shaikh MA, Shams-Beyranvand M, Shamsi M, Shamsizadeh M, Sharafi H, Sharafi K, Sharif M, Sharif-Alhoseini M, Sharma M, Sharma R, She J, Sheikh A, Shi P, Shibuya K, Shigematsu M, Shiri R, Shirkoohi R, Shishani K, Shiue I, Shokraneh F, Shoman H, Shrime MG, Si S, Siabani S, Siddiqi TJ, Sigfusdottir ID, Sigurvinsdottir R, Silva JP, Silveira DGA, Singam NSV, Singh JA, Singh NP, Singh V, Sinha DN, Skiadaresi E, Slepak ELN, Sliwa K, Smith DL, Smith M, Soares Filho AM, Sobaih BH, Sobhani S, Sobngwi E, Soneji SS, Soofi M, Soosaraei M, Sorensen RJD, Soriano JB, Soyiri IN, Sposato LA, Sreeramareddy CT, Srinivasan V, Stanaway JD, Stein DJ, Steiner C, Steiner TJ, Stokes MA, Stovner LJ, Subart ML, Sudaryanto A, Sufiyan MB, Sunguya BF, Sur PJ, Sutradhar I, Sykes BL, Sylte DO, Tabarés-Seisdedos R, Tadakamadla SK, Tadesse BT, Tandon N, Tassew SG, Tavakkoli M, Taveira N, Taylor HR, Tehrani-Banihashemi A, Tekalign TG, Tekelemedhin SW, Tekle MG, Temesgen H, Temsah MH, Temsah O, Terkawi AS, Teweldemedhin M, Thankappan KR, Thomas N, Tilahun B, To QG, Tonelli M, Topor-Madry R, Topouzis F, Torre AE, Tortajada-Girbés M, Touvier M, Tovani-Palone MR, Towbin JA, Tran BX, Tran KB, Troeger CE, Truelsen TC, Tsilimbaris MK, Tsoi D, Tudor Car L, Tuzcu EM, Ukwaja KN, Ullah I, Undurraga EA, Unutzer J, Updike RL, Usman MS, Uthman OA, Vaduganathan M, Vaezi A, Valdez PR, Varughese S, Vasankari TJ, Venketasubramanian N, Villafaina S, Violante FS, Vladimirov SK, Vlassov V, Vollset SE, Vosoughi K, Vujcic IS, Wagnew FS, Waheed Y, Waller SG, Wang Y, Wang YP, Weiderpass E, Weintraub RG, Weiss DJ, Weldegebreal F, Weldegwergs KG, Werdecker A, West TE, Whiteford HA, Widecka J, Wijeratne T, Wilner LB, Wilson S, Winkler AS, Wiyeh AB, Wiysonge CS, Wolfe CDA, Woolf AD, Wu S, Wu YC, Wyper GMA, Xavier D, Xu G, Yadgir S, Yadollahpour A, Yahyazadeh Jabbari SH, Yamada T, Yan LL, Yano Y, Yaseri M, Yasin YJ, Yeshaneh A, Yimer EM, Yip P, Yisma E, Yonemoto N, Yoon SJ, Yotebieng M, Younis MZ, Yousefifard M, Yu C, Zadnik V, Zaidi Z, Zaman SB, Zamani M, Zare Z, Zeleke AJ, Zenebe ZM, Zhang K, Zhao Z, Zhou M, Zodpey S, Zucker I, Vos T, Murray CJL. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018; 392:1789-1858. [PMID: 30496104 PMCID: PMC6227754 DOI: 10.1016/s0140-6736(18)32279-7] [Citation(s) in RCA: 7041] [Impact Index Per Article: 1173.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Revised: 08/30/2018] [Accepted: 09/12/2018] [Indexed: 02/06/2023]
Abstract
BACKGROUND The Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017) includes a comprehensive assessment of incidence, prevalence, and years lived with disability (YLDs) for 354 causes in 195 countries and territories from 1990 to 2017. Previous GBD studies have shown how the decline of mortality rates from 1990 to 2016 has led to an increase in life expectancy, an ageing global population, and an expansion of the non-fatal burden of disease and injury. These studies have also shown how a substantial portion of the world's population experiences non-fatal health loss with considerable heterogeneity among different causes, locations, ages, and sexes. Ongoing objectives of the GBD study include increasing the level of estimation detail, improving analytical strategies, and increasing the amount of high-quality data. METHODS We estimated incidence and prevalence for 354 diseases and injuries and 3484 sequelae. We used an updated and extensive body of literature studies, survey data, surveillance data, inpatient admission records, outpatient visit records, and health insurance claims, and additionally used results from cause of death models to inform estimates using a total of 68 781 data sources. Newly available clinical data from India, Iran, Japan, Jordan, Nepal, China, Brazil, Norway, and Italy were incorporated, as well as updated claims data from the USA and new claims data from Taiwan (province of China) and Singapore. We used DisMod-MR 2.1, a Bayesian meta-regression tool, as the main method of estimation, ensuring consistency between rates of incidence, prevalence, remission, and cause of death for each condition. YLDs were estimated as the product of a prevalence estimate and a disability weight for health states of each mutually exclusive sequela, adjusted for comorbidity. We updated the Socio-demographic Index (SDI), a summary development indicator of income per capita, years of schooling, and total fertility rate. Additionally, we calculated differences between male and female YLDs to identify divergent trends across sexes. GBD 2017 complies with the Guidelines for Accurate and Transparent Health Estimates Reporting. FINDINGS Globally, for females, the causes with the greatest age-standardised prevalence were oral disorders, headache disorders, and haemoglobinopathies and haemolytic anaemias in both 1990 and 2017. For males, the causes with the greatest age-standardised prevalence were oral disorders, headache disorders, and tuberculosis including latent tuberculosis infection in both 1990 and 2017. In terms of YLDs, low back pain, headache disorders, and dietary iron deficiency were the leading Level 3 causes of YLD counts in 1990, whereas low back pain, headache disorders, and depressive disorders were the leading causes in 2017 for both sexes combined. All-cause age-standardised YLD rates decreased by 3·9% (95% uncertainty interval [UI] 3·1-4·6) from 1990 to 2017; however, the all-age YLD rate increased by 7·2% (6·0-8·4) while the total sum of global YLDs increased from 562 million (421-723) to 853 million (642-1100). The increases for males and females were similar, with increases in all-age YLD rates of 7·9% (6·6-9·2) for males and 6·5% (5·4-7·7) for females. We found significant differences between males and females in terms of age-standardised prevalence estimates for multiple causes. The causes with the greatest relative differences between sexes in 2017 included substance use disorders (3018 cases [95% UI 2782-3252] per 100 000 in males vs s1400 [1279-1524] per 100 000 in females), transport injuries (3322 [3082-3583] vs 2336 [2154-2535]), and self-harm and interpersonal violence (3265 [2943-3630] vs 5643 [5057-6302]). INTERPRETATION Global all-cause age-standardised YLD rates have improved only slightly over a period spanning nearly three decades. However, the magnitude of the non-fatal disease burden has expanded globally, with increasing numbers of people who have a wide spectrum of conditions. A subset of conditions has remained globally pervasive since 1990, whereas other conditions have displayed more dynamic trends, with different ages, sexes, and geographies across the globe experiencing varying burdens and trends of health loss. This study emphasises how global improvements in premature mortality for select conditions have led to older populations with complex and potentially expensive diseases, yet also highlights global achievements in certain domains of disease and injury. FUNDING Bill & Melinda Gates Foundation.
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Roth GA, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, Abbastabar H, Abd-Allah F, Abdela J, Abdelalim A, Abdollahpour I, Abdulkader RS, Abebe HT, Abebe M, Abebe Z, Abejie AN, Abera SF, Abil OZ, Abraha HN, Abrham AR, Abu-Raddad LJ, Accrombessi MMK, Acharya D, Adamu AA, Adebayo OM, Adedoyin RA, Adekanmbi V, Adetokunboh OO, Adhena BM, Adib MG, Admasie A, Afshin A, Agarwal G, Agesa KM, Agrawal A, Agrawal S, Ahmadi A, Ahmadi M, Ahmed MB, Ahmed S, Aichour AN, Aichour I, Aichour MTE, Akbari ME, Akinyemi RO, Akseer N, Al-Aly Z, Al-Eyadhy A, Al-Raddadi RM, Alahdab F, Alam K, Alam T, Alebel A, Alene KA, Alijanzadeh M, Alizadeh-Navaei R, Aljunid SM, Alkerwi A, Alla F, Allebeck P, Alonso J, Altirkawi K, Alvis-Guzman N, Amare AT, Aminde LN, Amini E, Ammar W, Amoako YA, Anber NH, Andrei CL, Androudi S, Animut MD, Anjomshoa M, Ansari H, Ansha MG, Antonio CAT, Anwari P, Aremu O, Ärnlöv J, Arora A, Arora M, Artaman A, Aryal KK, Asayesh H, Asfaw ET, Ataro Z, Atique S, Atre SR, Ausloos M, Avokpaho EFGA, Awasthi A, Quintanilla BPA, Ayele Y, Ayer R, Azzopardi PS, Babazadeh A, Bacha U, Badali H, Badawi A, Bali AG, Ballesteros KE, Banach M, Banerjee K, Bannick MS, Banoub JAM, Barboza MA, Barker-Collo SL, Bärnighausen TW, Barquera S, Barrero LH, Bassat Q, Basu S, Baune BT, Baynes HW, Bazargan-Hejazi S, Bedi N, Beghi E, Behzadifar M, Behzadifar M, Béjot Y, Bekele BB, Belachew AB, Belay E, Belay YA, Bell ML, Bello AK, Bennett DA, Bensenor IM, Berman AE, Bernabe E, Bernstein RS, Bertolacci GJ, Beuran M, Beyranvand T, Bhalla A, Bhattarai S, Bhaumik S, Bhutta ZA, Biadgo B, Biehl MH, Bijani A, Bikbov B, Bilano V, Bililign N, Bin Sayeed MS, Bisanzio D, Biswas T, Blacker BF, Basara BB, Borschmann R, Bosetti C, Bozorgmehr K, Brady OJ, Brant LC, Brayne C, Brazinova A, Breitborde NJK, Brenner H, Briant PS, Britton G, Brugha T, Busse R, Butt ZA, Callender CSKH, Campos-Nonato IR, Campuzano Rincon JC, Cano J, Car M, Cárdenas R, Carreras G, Carrero JJ, Carter A, Carvalho F, Castañeda-Orjuela CA, Castillo Rivas J, Castle CD, Castro C, Castro F, Catalá-López F, Cerin E, Chaiah Y, Chang JC, Charlson FJ, Chaturvedi P, Chiang PPC, Chimed-Ochir O, Chisumpa VH, Chitheer A, Chowdhury R, Christensen H, Christopher DJ, Chung SC, Cicuttini FM, Ciobanu LG, Cirillo M, Cohen AJ, Cooper LT, Cortesi PA, Cortinovis M, Cousin E, Cowie BC, Criqui MH, Cromwell EA, Crowe CS, Crump JA, Cunningham M, Daba AK, Dadi AF, Dandona L, Dandona R, Dang AK, Dargan PI, Daryani A, Das SK, Gupta RD, Neves JD, Dasa TT, Dash AP, Davis AC, Davis Weaver N, Davitoiu DV, Davletov K, De La Hoz FP, De Neve JW, Degefa MG, Degenhardt L, Degfie TT, Deiparine S, Demoz GT, Demtsu BB, Denova-Gutiérrez E, Deribe K, Dervenis N, Des Jarlais DC, Dessie GA, Dey S, Dharmaratne SD, Dicker D, Dinberu MT, Ding EL, Dirac MA, Djalalinia S, Dokova K, Doku DT, Donnelly CA, Dorsey ER, Doshi PP, Douwes-Schultz D, Doyle KE, Driscoll TR, Dubey M, Dubljanin E, Duken EE, Duncan BB, Duraes AR, Ebrahimi H, Ebrahimpour S, Edessa D, Edvardsson D, Eggen AE, El Bcheraoui C, El Sayed Zaki M, El-Khatib Z, Elkout H, Ellingsen CL, Endres M, Endries AY, Er B, Erskine HE, Eshrati B, Eskandarieh S, Esmaeili R, Esteghamati A, Fakhar M, Fakhim H, Faramarzi M, Fareed M, Farhadi F, Farinha CSES, Faro A, Farvid MS, Farzadfar F, Farzaei MH, Feigin VL, Feigl AB, Fentahun N, Fereshtehnejad SM, Fernandes E, Fernandes JC, Ferrari AJ, Feyissa GT, Filip I, Finegold S, Fischer F, Fitzmaurice C, Foigt NA, Foreman KJ, Fornari C, Frank TD, Fukumoto T, Fuller JE, Fullman N, Fürst T, Furtado JM, Futran ND, Gallus S, Garcia-Basteiro AL, Garcia-Gordillo MA, Gardner WM, Gebre AK, Gebrehiwot TT, Gebremedhin AT, Gebremichael B, Gebremichael TG, Gelano TF, Geleijnse JM, Genova-Maleras R, Geramo YCD, Gething PW, Gezae KE, Ghadami MR, Ghadimi R, Ghasemi Falavarjani K, Ghasemi-Kasman M, Ghimire M, Gibney KB, Gill PS, Gill TK, Gillum RF, Ginawi IA, Giroud M, Giussani G, Goenka S, Goldberg EM, Goli S, Gómez-Dantés H, Gona PN, Gopalani SV, Gorman TM, Goto A, Goulart AC, Gnedovskaya EV, Grada A, Grosso G, Gugnani HC, Guimaraes ALS, Guo Y, Gupta PC, Gupta R, Gupta R, Gupta T, Gutiérrez RA, Gyawali B, Haagsma JA, Hafezi-Nejad N, Hagos TB, Hailegiyorgis TT, Hailu GB, Haj-Mirzaian A, Haj-Mirzaian A, Hamadeh RR, Hamidi S, Handal AJ, Hankey GJ, Harb HL, Harikrishnan S, Haro JM, Hasan M, Hassankhani H, Hassen HY, Havmoeller R, Hay RJ, Hay SI, He Y, Hedayatizadeh-Omran A, Hegazy MI, Heibati B, Heidari M, Hendrie D, Henok A, Henry NJ, Herteliu C, Heydarpour F, Heydarpour P, Heydarpour S, Hibstu DT, Hoek HW, Hole MK, Homaie Rad E, Hoogar P, Hosgood HD, Hosseini SM, Hosseinzadeh M, Hostiuc M, Hostiuc S, Hotez PJ, Hoy DG, Hsiao T, Hu G, Huang JJ, Husseini A, Hussen MM, Hutfless S, Idrisov B, Ilesanmi OS, Iqbal U, Irvani SSN, Irvine CMS, Islam N, Islam SMS, Islami F, Jacobsen KH, Jahangiry L, Jahanmehr N, Jain SK, Jakovljevic M, Jalu MT, James SL, Javanbakht M, Jayatilleke AU, Jeemon P, Jenkins KJ, Jha RP, Jha V, Johnson CO, Johnson SC, Jonas JB, Joshi A, Jozwiak JJ, Jungari SB, Jürisson M, Kabir Z, Kadel R, Kahsay A, Kalani R, Karami M, Karami Matin B, Karch A, Karema C, Karimi-Sari H, Kasaeian A, Kassa DH, Kassa GM, Kassa TD, Kassebaum NJ, Katikireddi SV, Kaul A, Kazemi Z, Karyani AK, Kazi DS, Kefale AT, Keiyoro PN, Kemp GR, Kengne AP, Keren A, Kesavachandran CN, Khader YS, Khafaei B, Khafaie MA, Khajavi A, Khalid N, Khalil IA, Khan EA, Khan MS, Khan MA, Khang YH, Khater MM, Khoja AT, Khosravi A, Khosravi MH, Khubchandani J, Kiadaliri AA, Kibret GD, Kidanemariam ZT, Kiirithio DN, Kim D, Kim YE, Kim YJ, Kimokoti RW, Kinfu Y, Kisa A, Kissimova-Skarbek K, Kivimäki M, Knudsen AKS, Kocarnik JM, Kochhar S, Kokubo Y, Kolola T, Kopec JA, Koul PA, Koyanagi A, Kravchenko MA, Krishan K, Kuate Defo B, Kucuk Bicer B, Kumar GA, Kumar M, Kumar P, Kutz MJ, Kuzin I, Kyu HH, Lad DP, Lad SD, Lafranconi A, Lal DK, Lalloo R, Lallukka T, Lam JO, Lami FH, Lansingh VC, Lansky S, Larson HJ, Latifi A, Lau KMM, Lazarus JV, Lebedev G, Lee PH, Leigh J, Leili M, Leshargie CT, Li S, Li Y, Liang J, Lim LL, Lim SS, Limenih MA, Linn S, Liu S, Liu Y, Lodha R, Lonsdale C, Lopez AD, Lorkowski S, Lotufo PA, Lozano R, Lunevicius R, Ma S, Macarayan ERK, Mackay MT, MacLachlan JH, Maddison ER, Madotto F, Magdy Abd El Razek H, Magdy Abd El Razek M, Maghavani DP, Majdan M, Majdzadeh R, Majeed A, Malekzadeh R, Malta DC, Manda AL, Mandarano-Filho LG, Manguerra H, Mansournia MA, Mapoma CC, Marami D, Maravilla JC, Marcenes W, Marczak L, Marks A, Marks GB, Martinez G, Martins-Melo FR, Martopullo I, März W, Marzan MB, Masci JR, Massenburg BB, Mathur MR, Mathur P, Matzopoulos R, Maulik PK, Mazidi M, McAlinden C, McGrath JJ, McKee M, McMahon BJ, Mehata S, Mehndiratta MM, Mehrotra R, Mehta KM, Mehta V, Mekonnen TC, Melese A, Melku M, Memiah PTN, Memish ZA, Mendoza W, Mengistu DT, Mengistu G, Mensah GA, Mereta ST, Meretoja A, Meretoja TJ, Mestrovic T, Mezgebe HB, Miazgowski B, Miazgowski T, Millear AI, Miller TR, Miller-Petrie MK, Mini GK, Mirabi P, Mirarefin M, Mirica A, Mirrakhimov EM, Misganaw AT, Mitiku H, Moazen B, Mohammad KA, Mohammadi M, Mohammadifard N, Mohammed MA, Mohammed S, Mohan V, Mokdad AH, Molokhia M, Monasta L, Moradi G, Moradi-Lakeh M, Moradinazar M, Moraga P, Morawska L, Moreno Velásquez I, Morgado-Da-Costa J, Morrison SD, Moschos MM, Mouodi S, Mousavi SM, Muchie KF, Mueller UO, Mukhopadhyay S, Muller K, Mumford JE, Musa J, Musa KI, Mustafa G, Muthupandian S, Nachega JB, Nagel G, Naheed A, Nahvijou A, Naik G, Nair S, Najafi F, Naldi L, Nam HS, Nangia V, Nansseu JR, Nascimento BR, Natarajan G, Neamati N, Negoi I, Negoi RI, Neupane S, Newton CRJ, Ngalesoni FN, Ngunjiri JW, Nguyen AQ, Nguyen G, Nguyen HT, Nguyen HT, Nguyen LH, Nguyen M, Nguyen TH, Nichols E, Ningrum DNA, Nirayo YL, Nixon MR, Nolutshungu N, Nomura S, Norheim OF, Noroozi M, Norrving B, Noubiap JJ, Nouri HR, Nourollahpour Shiadeh M, Nowroozi MR, Nyasulu PS, Odell CM, Ofori-Asenso R, Ogbo FA, Oh IH, Oladimeji O, Olagunju AT, Olivares PR, Olsen HE, Olusanya BO, Olusanya JO, Ong KL, Ong SKS, Oren E, Orpana HM, Ortiz A, Ortiz JR, Otstavnov SS, Øverland S, Owolabi MO, Özdemir R, P A M, Pacella R, Pakhale S, Pakhare AP, Pakpour AH, Pana A, Panda-Jonas S, Pandian JD, Parisi A, Park EK, Parry CDH, Parsian H, Patel S, Pati S, Patton GC, Paturi VR, Paulson KR, Pereira A, Pereira DM, Perico N, Pesudovs K, Petzold M, Phillips MR, Piel FB, Pigott DM, Pillay JD, Pirsaheb M, Pishgar F, Polinder S, Postma MJ, Pourshams A, Poustchi H, Pujar A, Prakash S, Prasad N, Purcell CA, Qorbani M, Quintana H, Quistberg DA, Rade KW, Radfar A, Rafay A, Rafiei A, Rahim F, Rahimi K, Rahimi-Movaghar A, Rahman M, Rahman MHU, Rahman MA, Rai RK, Rajsic S, Ram U, Ranabhat CL, Ranjan P, Rao PC, Rawaf DL, Rawaf S, Razo-García C, Reddy KS, Reiner RC, Reitsma MB, Remuzzi G, Renzaho AMN, Resnikoff S, Rezaei S, Rezaeian S, Rezai MS, Riahi SM, Ribeiro ALP, Rios-Blancas MJ, Roba KT, Roberts NLS, Robinson SR, Roever L, Ronfani L, Roshandel G, Rostami A, Rothenbacher D, Roy A, Rubagotti E, Sachdev PS, Saddik B, Sadeghi E, Safari H, Safdarian M, Safi S, Safiri S, Sagar R, Sahebkar A, Sahraian MA, Salam N, Salama JS, Salamati P, Saldanha RDF, Saleem Z, Salimi Y, Salvi SS, Salz I, Sambala EZ, Samy AM, Sanabria J, Sanchez-Niño MD, Santomauro DF, Santos IS, Santos JV, Milicevic MMS, Sao Jose BP, Sarker AR, Sarmiento-Suárez R, Sarrafzadegan N, Sartorius B, Sarvi S, Sathian B, Satpathy M, Sawant AR, Sawhney M, Saxena S, Sayyah M, Schaeffner E, Schmidt MI, Schneider IJC, Schöttker B, Schutte AE, Schwebel DC, Schwendicke F, Scott JG, Sekerija M, Sepanlou SG, Serván-Mori E, Seyedmousavi S, Shabaninejad H, Shackelford KA, Shafieesabet A, Shahbazi M, Shaheen AA, Shaikh MA, Shams-Beyranvand M, Shamsi M, Shamsizadeh M, Sharafi K, Sharif M, Sharif-Alhoseini M, Sharma R, She J, Sheikh A, Shi P, Shiferaw MS, Shigematsu M, Shiri R, Shirkoohi R, Shiue I, Shokraneh F, Shrime MG, Si S, Siabani S, Siddiqi TJ, Sigfusdottir ID, Sigurvinsdottir R, Silberberg DH, Silva DAS, Silva JP, Silva NTD, Silveira DGA, Singh JA, Singh NP, Singh PK, Singh V, Sinha DN, Sliwa K, Smith M, Sobaih BH, Sobhani S, Sobngwi E, Soneji SS, Soofi M, Sorensen RJD, Soriano JB, Soyiri IN, Sposato LA, Sreeramareddy CT, Srinivasan V, Stanaway JD, Starodubov VI, Stathopoulou V, Stein DJ, Steiner C, Stewart LG, Stokes MA, Subart ML, Sudaryanto A, Sufiyan MB, Sur PJ, Sutradhar I, Sykes BL, Sylaja PN, Sylte DO, Szoeke CEI, Tabarés-Seisdedos R, Tabuchi T, Tadakamadla SK, Takahashi K, Tandon N, Tassew SG, Taveira N, Tehrani-Banihashemi A, Tekalign TG, Tekle MG, Temsah MH, Temsah O, Terkawi AS, Teshale MY, Tessema B, Tessema GA, Thankappan KR, Thirunavukkarasu S, Thomas N, Thrift AG, Thurston GD, Tilahun B, To QG, Tobe-Gai R, Tonelli M, Topor-Madry R, Torre AE, Tortajada-Girbés M, Touvier M, Tovani-Palone MR, Tran BX, Tran KB, Tripathi S, Troeger CE, Truelsen TC, Truong NT, Tsadik AG, Tsoi D, Tudor Car L, Tuzcu EM, Tyrovolas S, Ukwaja KN, Ullah I, Undurraga EA, Updike RL, Usman MS, Uthman OA, Uzun SB, Vaduganathan M, Vaezi A, Vaidya G, Valdez PR, Varavikova E, Vasankari TJ, Venketasubramanian N, Villafaina S, Violante FS, Vladimirov SK, Vlassov V, Vollset SE, Vos T, Wagner GR, Wagnew FS, Waheed Y, Wallin MT, Walson JL, Wang Y, Wang YP, Wassie MM, Weiderpass E, Weintraub RG, Weldegebreal F, Weldegwergs KG, Werdecker A, Werkneh AA, West TE, Westerman R, Whiteford HA, Widecka J, Wilner LB, Wilson S, Winkler AS, Wiysonge CS, Wolfe CDA, Wu S, Wu YC, Wyper GMA, Xavier D, Xu G, Yadgir S, Yadollahpour A, Yahyazadeh Jabbari SH, Yakob B, Yan LL, Yano Y, Yaseri M, Yasin YJ, Yentür GK, Yeshaneh A, Yimer EM, Yip P, Yirsaw BD, Yisma E, Yonemoto N, Yonga G, Yoon SJ, Yotebieng M, Younis MZ, Yousefifard M, Yu C, Zadnik V, Zaidi Z, Zaman SB, Zamani M, Zare Z, Zeleke AJ, Zenebe ZM, Zhang AL, Zhang K, Zhou M, Zodpey S, Zuhlke LJ, Naghavi M, Murray CJL. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018; 392:1736-1788. [PMID: 30496103 PMCID: PMC6227606 DOI: 10.1016/s0140-6736%2818%2932203-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2018] [Revised: 08/29/2018] [Accepted: 08/30/2018] [Indexed: 01/19/2024]
Abstract
BACKGROUND Global development goals increasingly rely on country-specific estimates for benchmarking a nation's progress. To meet this need, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 estimated global, regional, national, and, for selected locations, subnational cause-specific mortality beginning in the year 1980. Here we report an update to that study, making use of newly available data and improved methods. GBD 2017 provides a comprehensive assessment of cause-specific mortality for 282 causes in 195 countries and territories from 1980 to 2017. METHODS The causes of death database is composed of vital registration (VR), verbal autopsy (VA), registry, survey, police, and surveillance data. GBD 2017 added ten VA studies, 127 country-years of VR data, 502 cancer-registry country-years, and an additional surveillance country-year. Expansions of the GBD cause of death hierarchy resulted in 18 additional causes estimated for GBD 2017. Newly available data led to subnational estimates for five additional countries-Ethiopia, Iran, New Zealand, Norway, and Russia. Deaths assigned International Classification of Diseases (ICD) codes for non-specific, implausible, or intermediate causes of death were reassigned to underlying causes by redistribution algorithms that were incorporated into uncertainty estimation. We used statistical modelling tools developed for GBD, including the Cause of Death Ensemble model (CODEm), to generate cause fractions and cause-specific death rates for each location, year, age, and sex. Instead of using UN estimates as in previous versions, GBD 2017 independently estimated population size and fertility rate for all locations. Years of life lost (YLLs) were then calculated as the sum of each death multiplied by the standard life expectancy at each age. All rates reported here are age-standardised. FINDINGS At the broadest grouping of causes of death (Level 1), non-communicable diseases (NCDs) comprised the greatest fraction of deaths, contributing to 73·4% (95% uncertainty interval [UI] 72·5-74·1) of total deaths in 2017, while communicable, maternal, neonatal, and nutritional (CMNN) causes accounted for 18·6% (17·9-19·6), and injuries 8·0% (7·7-8·2). Total numbers of deaths from NCD causes increased from 2007 to 2017 by 22·7% (21·5-23·9), representing an additional 7·61 million (7·20-8·01) deaths estimated in 2017 versus 2007. The death rate from NCDs decreased globally by 7·9% (7·0-8·8). The number of deaths for CMNN causes decreased by 22·2% (20·0-24·0) and the death rate by 31·8% (30·1-33·3). Total deaths from injuries increased by 2·3% (0·5-4·0) between 2007 and 2017, and the death rate from injuries decreased by 13·7% (12·2-15·1) to 57·9 deaths (55·9-59·2) per 100 000 in 2017. Deaths from substance use disorders also increased, rising from 284 000 deaths (268 000-289 000) globally in 2007 to 352 000 (334 000-363 000) in 2017. Between 2007 and 2017, total deaths from conflict and terrorism increased by 118·0% (88·8-148·6). A greater reduction in total deaths and death rates was observed for some CMNN causes among children younger than 5 years than for older adults, such as a 36·4% (32·2-40·6) reduction in deaths from lower respiratory infections for children younger than 5 years compared with a 33·6% (31·2-36·1) increase in adults older than 70 years. Globally, the number of deaths was greater for men than for women at most ages in 2017, except at ages older than 85 years. Trends in global YLLs reflect an epidemiological transition, with decreases in total YLLs from enteric infections, respiratory infections and tuberculosis, and maternal and neonatal disorders between 1990 and 2017; these were generally greater in magnitude at the lowest levels of the Socio-demographic Index (SDI). At the same time, there were large increases in YLLs from neoplasms and cardiovascular diseases. YLL rates decreased across the five leading Level 2 causes in all SDI quintiles. The leading causes of YLLs in 1990-neonatal disorders, lower respiratory infections, and diarrhoeal diseases-were ranked second, fourth, and fifth, in 2017. Meanwhile, estimated YLLs increased for ischaemic heart disease (ranked first in 2017) and stroke (ranked third), even though YLL rates decreased. Population growth contributed to increased total deaths across the 20 leading Level 2 causes of mortality between 2007 and 2017. Decreases in the cause-specific mortality rate reduced the effect of population growth for all but three causes: substance use disorders, neurological disorders, and skin and subcutaneous diseases. INTERPRETATION Improvements in global health have been unevenly distributed among populations. Deaths due to injuries, substance use disorders, armed conflict and terrorism, neoplasms, and cardiovascular disease are expanding threats to global health. For causes of death such as lower respiratory and enteric infections, more rapid progress occurred for children than for the oldest adults, and there is continuing disparity in mortality rates by sex across age groups. Reductions in the death rate of some common diseases are themselves slowing or have ceased, primarily for NCDs, and the death rate for selected causes has increased in the past decade. FUNDING Bill & Melinda Gates Foundation.
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Lozano R, Fullman N, Abate D, Abay SM, Abbafati C, Abbasi N, Abbastabar H, Abd-Allah F, Abdela J, Abdelalim A, Abdel-Rahman O, Abdi A, Abdollahpour I, Abdulkader RS, Abebe ND, Abebe Z, Abejie AN, Abera SF, Abil OZ, Aboyans V, Abraha HN, Abrham AR, Abu-Raddad LJ, Abu-Rmeileh NM, Abyu GY, Accrombessi MMK, Acharya D, Acharya P, Adamu AA, Adebayo OM, Adedeji IA, Adedoyin RA, Adekanmbi V, Adetokunboh OO, Adhena BM, Adhikari TB, Adib MG, Adou AK, Adsuar JC, Afarideh M, Afshari M, Afshin A, Agarwal G, Aghayan SA, Agius D, Agrawal A, Agrawal S, Ahmadi A, Ahmadi M, Ahmadieh H, Ahmed MB, Ahmed S, Akalu TY, Akanda AS, Akbari ME, Akibu M, Akinyemi RO, Akinyemiju T, Akseer N, Alahdab F, Al-Aly Z, Alam K, Alam T, Albujeer A, Alebel A, Alene KA, Al-Eyadhy A, Alhabib S, Ali R, Alijanzadeh M, Alizadeh-Navaei R, Aljunid SM, Alkerwi A, Alla F, Allebeck P, Allen CA, Almasi A, Al-Maskari F, Al-Mekhlafi HM, Alonso J, Al-Raddadi RM, Alsharif U, Altirkawi K, Alvis-Guzman N, Amare AT, Amenu K, Amini E, Ammar W, Anber NH, Anderson JA, Andrei CL, Androudi S, Animut MD, Anjomshoa M, Ansari H, Ansariadi A, Ansha MG, Antonio CAT, Anwari P, Appiah LT, Aremu O, Areri HA, Ärnlöv J, Arora M, Aryal KK, Asayesh H, Asfaw ET, Asgedom SW, Asghar RJ, Assadi R, Ataro Z, Atique S, Atre SR, Atteraya MS, Ausloos M, Avila-Burgos L, Avokpaho EFGA, Awasthi A, Ayala Quintanilla BP, Ayele HT, Ayele Y, Ayer R, Azarpazhooh MR, Azzopardi PS, Azzopardi-Muscat N, Babalola TK, Babazadeh A, Badali H, Badawi A, Balakrishnan K, Bali AG, Banach M, Banerjee A, Banoub JAM, Banstola A, Barac A, Barboza MA, Barker-Collo SL, Bärnighausen TW, Barrero LH, Barthelemy CM, Bassat Q, Basu A, Basu S, Battista RJ, Baune BT, Baynes HW, Bazargan-Hejazi S, Bedi N, Beghi E, Behzadifar M, Behzadifar M, Béjot Y, Bekele BB, Belachew AB, Belay AG, Belay SA, Belay YA, Bell ML, Bello AK, Bennett DA, Bensenor IM, Benzian H, Berhane A, Berhe AK, Berman AE, Bernabe E, Bernstein RS, Bertolacci GJ, Beuran M, Beyranvand T, Bhala N, Bhalla A, Bhansali 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Zucker I, Zuhlke LJJ, Lim SS, Murray CJL. Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018; 392:2091-2138. [PMID: 30496107 PMCID: PMC6227911 DOI: 10.1016/s0140-6736(18)32281-5] [Citation(s) in RCA: 264] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 09/06/2018] [Accepted: 09/12/2018] [Indexed: 02/07/2023]
Abstract
BACKGROUND Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of "leaving no one behind", it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990-2017, projected indicators to 2030, and analysed global attainment. METHODS We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the health-related SDG index, we transformed the value for each indicator on a scale of 0-100, with 0 as the 2·5th percentile and 100 as the 97·5th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator. FINDINGS The global median health-related SDG index in 2017 was 59·4 (IQR 35·4-67·3), ranging from a low of 11·6 (95% uncertainty interval 9·6-14·0) to a high of 84·9 (83·1-86·7). SDG index values in countries assessed at the subnational level varied substantially, particularly in China and India, although scores in Japan and the UK were more homogeneous. Indicators also varied by SDI quintile and sex, with males having worse outcomes than females for non-communicable disease (NCD) mortality, alcohol use, and smoking, among others. Most countries were projected to have a higher health-related SDG index in 2030 than in 2017, while country-level probabilities of attainment by 2030 varied widely by indicator. Under-5 mortality, neonatal mortality, maternal mortality ratio, and malaria indicators had the most countries with at least 95% probability of target attainment. Other indicators, including NCD mortality and suicide mortality, had no countries projected to meet corresponding SDG targets on the basis of projected mean values for 2030 but showed some probability of attainment by 2030. For some indicators, including child malnutrition, several infectious diseases, and most violence measures, the annualised rates of change required to meet SDG targets far exceeded the pace of progress achieved by any country in the recent past. We found that applying the mean global annualised rate of change to indicators without defined targets would equate to about 19% and 22% reductions in global smoking and alcohol consumption, respectively; a 47% decline in adolescent birth rates; and a more than 85% increase in health worker density per 1000 population by 2030. INTERPRETATION The GBD study offers a unique, robust platform for monitoring the health-related SDGs across demographic and geographic dimensions. Our findings underscore the importance of increased collection and analysis of disaggregated data and highlight where more deliberate design or targeting of interventions could accelerate progress in attaining the SDGs. Current projections show that many health-related SDG indicators, NCDs, NCD-related risks, and violence-related indicators will require a concerted shift away from what might have driven past gains-curative interventions in the case of NCDs-towards multisectoral, prevention-oriented policy action and investments to achieve SDG aims. Notably, several targets, if they are to be met by 2030, demand a pace of progress that no country has achieved in the recent past. The future is fundamentally uncertain, and no model can fully predict what breakthroughs or events might alter the course of the SDGs. What is clear is that our actions-or inaction-today will ultimately dictate how close the world, collectively, can get to leaving no one behind by 2030. FUNDING Bill & Melinda Gates Foundation.
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Stanaway JD, Afshin A, Gakidou E, Lim SS, Abate D, Abate KH, Abbafati C, Abbasi N, Abbastabar H, Abd-Allah F, Abdela J, Abdelalim A, Abdollahpour I, Abdulkader RS, Abebe M, Abebe Z, Abera SF, Abil OZ, Abraha HN, Abrham AR, Abu-Raddad LJ, Abu-Rmeileh NME, Accrombessi MMK, Acharya D, Acharya P, Adamu AA, Adane AA, Adebayo OM, Adedoyin RA, Adekanmbi V, Ademi Z, Adetokunboh OO, Adib MG, Admasie A, Adsuar JC, Afanvi KA, Afarideh M, Agarwal G, Aggarwal A, Aghayan SA, Agrawal A, Agrawal S, Ahmadi A, Ahmadi M, Ahmadieh H, Ahmed MB, Aichour AN, Aichour I, Aichour MTE, Akbari ME, Akinyemiju T, Akseer N, Al-Aly Z, Al-Eyadhy A, Al-Mekhlafi HM, Alahdab F, Alam K, Alam S, Alam T, Alashi A, Alavian SM, Alene KA, Ali K, Ali SM, Alijanzadeh M, Alizadeh-Navaei R, Aljunid SM, Alkerwi A, Alla F, Alsharif U, Altirkawi K, Alvis-Guzman N, Amare AT, Ammar W, Anber NH, Anderson JA, Andrei CL, Androudi S, Animut MD, Anjomshoa M, Ansha MG, Antó JM, Antonio CAT, Anwari P, Appiah LT, Appiah SCY, Arabloo J, Aremu O, Ärnlöv J, Artaman A, Aryal KK, Asayesh H, Ataro Z, Ausloos M, Avokpaho EFGA, Awasthi A, Ayala Quintanilla BP, Ayer R, Ayuk TB, Azzopardi PS, Babazadeh A, Badali H, Badawi A, Balakrishnan K, Bali AG, Ball K, Ballew SH, Banach M, Banoub JAM, Barac A, Barker-Collo SL, Bärnighausen TW, Barrero LH, Basu S, Baune BT, Bazargan-Hejazi S, Bedi N, Beghi E, Behzadifar M, Behzadifar M, Béjot Y, Bekele BB, Bekru ET, Belay E, Belay YA, Bell ML, Bello AK, Bennett DA, Bensenor IM, Bergeron G, Berhane A, Bernabe E, Bernstein RS, Beuran M, Beyranvand T, Bhala N, Bhalla A, Bhattarai S, Bhutta ZA, Biadgo B, Bijani A, Bikbov B, Bilano V, Bililign N, Bin Sayeed MS, Bisanzio D, Biswas T, Bjørge T, Blacker BF, Bleyer A, Borschmann R, Bou-Orm IR, Boufous S, Bourne R, Brady OJ, Brauer M, Brazinova A, Breitborde NJK, Brenner H, Briko AN, Britton G, Brugha T, Buchbinder R, Burnett RT, Busse R, Butt ZA, Cahill LE, Cahuana-Hurtado L, Campos-Nonato IR, Cárdenas R, Carreras G, Carrero JJ, Carvalho F, Castañeda-Orjuela CA, Castillo Rivas J, Castro F, Catalá-López F, Causey K, Cercy KM, Cerin E, Chaiah Y, Chang HY, Chang JC, Chang KL, Charlson FJ, Chattopadhyay A, Chattu VK, Chee ML, Cheng CY, Chew A, Chiang PPC, Chimed-Ochir O, Chin KL, Chitheer A, Choi JYJ, Chowdhury R, Christensen H, Christopher DJ, Chung SC, Cicuttini FM, Cirillo M, Cohen AJ, Collado-Mateo D, Cooper C, Cooper OR, Coresh J, Cornaby L, Cortesi PA, Cortinovis M, Costa M, Cousin E, Criqui MH, Cromwell EA, Cundiff DK, Daba AK, Dachew BA, Dadi AF, Damasceno AAM, Dandona L, Dandona R, Darby SC, Dargan PI, Daryani A, Das Gupta R, Das Neves J, Dasa TT, Dash AP, Davitoiu DV, Davletov K, De la Cruz-Góngora V, De La Hoz FP, De Leo D, De Neve JW, Degenhardt L, Deiparine S, Dellavalle RP, Demoz GT, Denova-Gutiérrez E, Deribe K, Dervenis N, Deshpande A, Des Jarlais DC, Dessie GA, Deveber GA, Dey S, Dharmaratne SD, Dhimal M, Dinberu MT, Ding EL, Diro HD, Djalalinia S, Do HP, Dokova K, Doku DT, Doyle KE, Driscoll TR, Dubey M, Dubljanin E, Duken EE, Duncan BB, Duraes AR, Ebert N, Ebrahimi H, Ebrahimpour S, Edvardsson D, Effiong A, Eggen AE, El Bcheraoui C, El-Khatib Z, Elyazar IR, Enayati A, Endries AY, Er B, Erskine HE, Eskandarieh S, Esteghamati A, Estep K, Fakhim H, Faramarzi M, Fareed M, Farid TA, Farinha CSES, Farioli A, Faro A, Farvid MS, Farzaei MH, Fatima B, Fay KA, Fazaeli AA, Feigin VL, Feigl AB, Fereshtehnejad SM, Fernandes E, Fernandes JC, Ferrara G, Ferrari AJ, Ferreira ML, Filip I, Finger JD, Fischer F, Foigt NA, Foreman KJ, Fukumoto T, Fullman N, Fürst T, Furtado JM, Futran ND, Gall S, Gallus S, Gamkrelidze A, Ganji M, Garcia-Basteiro AL, Gardner WM, Gebre AK, Gebremedhin AT, Gebremichael TG, Gelano TF, Geleijnse JM, Geramo YCD, Gething PW, Gezae KE, Ghadimi R, Ghadiri K, Ghasemi Falavarjani K, Ghasemi-Kasman M, Ghimire M, Ghosh R, Ghoshal AG, Giampaoli S, Gill PS, Gill TK, Gillum RF, Ginawi IA, Giussani G, Gnedovskaya EV, Godwin WW, Goli S, Gómez-Dantés H, Gona PN, Gopalani SV, Goulart AC, Grada A, Grams ME, Grosso G, Gugnani HC, Guo Y, Gupta R, Gupta R, Gupta T, Gutiérrez RA, Gutiérrez-Torres DS, Haagsma JA, Habtewold TD, Hachinski V, Hafezi-Nejad N, Hagos TB, Hailegiyorgis TT, Hailu GB, Haj-Mirzaian A, Haj-Mirzaian A, Hamadeh RR, Hamidi S, Handal AJ, Hankey GJ, Hao Y, Harb HL, Harikrishnan S, Haro JM, Hassankhani H, Hassen HY, Havmoeller R, Hawley CN, Hay SI, Hedayatizadeh-Omran A, Heibati B, Heidari B, Heidari M, Hendrie D, Henok A, Heredia-Pi I, Herteliu C, Heydarpour F, Heydarpour S, Hibstu DT, Higazi TB, Hilawe EH, Hoek HW, Hoffman HJ, Hole MK, Homaie Rad E, Hoogar P, Hosgood HD, Hosseini SM, Hosseinzadeh M, Hostiuc M, Hostiuc S, Hoy DG, Hsairi M, Hsiao T, Hu G, Hu H, Huang JJ, Hussen MA, Huynh CK, Iburg KM, Ikeda N, Ilesanmi OS, Iqbal U, Irvani SSN, Irvine CMS, Islam SMS, Islami F, Jackson MD, Jacobsen KH, Jahangiry L, Jahanmehr N, Jain SK, Jakovljevic M, James SL, Jassal SK, Jayatilleke AU, Jeemon P, Jha RP, Jha V, Ji JS, Jonas JB, Jonnagaddala J, Jorjoran Shushtari Z, Joshi A, Jozwiak JJ, Jürisson M, Kabir Z, Kahsay A, Kalani R, Kanchan T, Kant S, Kar C, Karami M, Karami Matin B, Karch A, Karema C, Karimi N, Karimi SM, Kasaeian A, Kassa DH, Kassa GM, Kassa TD, Kassebaum NJ, Katikireddi SV, Kaul A, Kawakami N, Kazemi Z, Karyani AK, Kefale AT, Keiyoro PN, Kemp GR, Kengne AP, Keren A, Kesavachandran CN, Khader YS, Khafaei B, Khafaie MA, Khajavi A, Khalid N, Khalil IA, Khan G, Khan MS, Khan MA, Khang YH, Khater MM, Khazaei M, Khazaie H, Khoja AT, Khosravi A, Khosravi MH, Kiadaliri AA, Kiirithio DN, Kim CI, Kim D, Kim YE, Kim YJ, Kimokoti RW, Kinfu Y, Kisa A, Kissimova-Skarbek K, Kivimäki M, Knibbs LD, Knudsen AKS, Kochhar S, Kokubo Y, Kolola T, Kopec JA, Kosen S, Koul PA, Koyanagi A, Kravchenko MA, Krishan K, Krohn KJ, Kromhout H, Kuate Defo B, Kucuk Bicer B, Kumar GA, Kumar M, Kuzin I, Kyu HH, Lachat C, Lad DP, Lad SD, Lafranconi A, Lalloo R, Lallukka T, Lami FH, Lang JJ, Lansingh VC, Larson SL, Latifi A, Lazarus JV, Lee PH, Leigh J, Leili M, Leshargie CT, Leung J, Levi M, Lewycka S, Li S, Li Y, Liang J, Liang X, Liao Y, Liben ML, Lim LL, Linn S, Liu S, Lodha R, Logroscino G, Lopez AD, Lorkowski S, Lotufo PA, Lozano R, Lucas TCD, Lunevicius R, Ma S, Macarayan ERK, Machado ÍE, Madotto F, Mai HT, Majdan M, Majdzadeh R, Majeed A, Malekzadeh R, Malta DC, Mamun AA, Manda AL, Manguerra H, Mansournia MA, Mantovani LG, Maravilla JC, Marcenes W, Marks A, Martin RV, Martins SCO, Martins-Melo FR, März W, Marzan MB, Massenburg BB, Mathur MR, Mathur P, Matsushita K, Maulik PK, Mazidi M, McAlinden C, McGrath JJ, McKee M, Mehrotra R, Mehta KM, Mehta V, Meier T, Mekonnen FA, Melaku YA, Melese A, Melku M, Memiah PTN, Memish ZA, Mendoza W, Mengistu DT, Mensah GA, Mensink GBM, Mereta ST, Meretoja A, Meretoja TJ, Mestrovic T, Mezgebe HB, Miazgowski B, Miazgowski T, Millear AI, Miller TR, Miller-Petrie MK, Mini GK, Mirarefin M, Mirica A, Mirrakhimov EM, Misganaw AT, Mitiku H, Moazen B, Mohajer B, Mohammad KA, Mohammadi M, Mohammadifard N, Mohammadnia-Afrouzi M, Mohammed S, Mohebi F, Mokdad AH, Molokhia M, Momeniha F, Monasta L, Moodley Y, Moradi G, Moradi-Lakeh M, Moradinazar M, Moraga P, Morawska L, Morgado-Da-Costa J, Morrison SD, Moschos MM, Mouodi S, Mousavi SM, Mozaffarian D, Mruts KB, Muche AA, Muchie KF, Mueller UO, Muhammed OS, Mukhopadhyay S, Muller K, Musa KI, Mustafa G, Nabhan AF, Naghavi M, Naheed A, Nahvijou A, Naik G, Naik N, Najafi F, Nangia V, Nansseu JR, Nascimento BR, Neal B, Neamati N, Negoi I, Negoi RI, Neupane S, Newton CRJ, Ngunjiri JW, Nguyen AQ, Nguyen G, Nguyen HT, Nguyen HLT, Nguyen HT, Nguyen M, Nguyen NB, Nichols E, Nie J, Ningrum DNA, Nirayo YL, Nishi N, Nixon MR, Nojomi M, Nomura S, Norheim OF, Noroozi M, Norrving B, Noubiap JJ, Nouri HR, Nourollahpour Shiadeh M, Nowroozi MR, Nsoesie EO, Nyasulu PS, Obermeyer CM, Odell CM, Ofori-Asenso R, Ogbo FA, Oh IH, Oladimeji O, Olagunju AT, Olagunju TO, Olivares PR, Olsen HE, Olusanya BO, Olusanya JO, Ong KL, Ong SK, Oren E, Orpana HM, Ortiz A, Ota E, Otstavnov SS, Øverland S, Owolabi MO, P A M, Pacella R, Pakhare AP, Pakpour AH, Pana A, Panda-Jonas S, Park EK, Parry CDH, Parsian H, Patel S, Pati S, Patil ST, Patle A, Patton GC, Paudel D, Paulson KR, Paz Ballesteros WC, Pearce N, Pereira A, Pereira DM, Perico N, Pesudovs K, Petzold M, Pham HQ, Phillips MR, Pillay JD, Piradov MA, Pirsaheb M, Pischon T, Pishgar F, Plana-Ripoll O, Plass D, Polinder S, Polkinghorne KR, Postma MJ, Poulton R, Pourshams A, Poustchi H, Prabhakaran D, Prakash S, Prasad N, Purcell CA, Purwar MB, Qorbani M, Radfar A, Rafay A, Rafiei A, Rahim F, Rahimi Z, Rahimi-Movaghar A, Rahimi-Movaghar V, Rahman M, Rahman MHU, Rahman MA, Rai RK, Rajati F, Rajsic S, Raju SB, Ram U, Ranabhat CL, Ranjan P, Rath GK, Rawaf DL, Rawaf S, Reddy KS, Rehm CD, Rehm J, Reiner RC, Reitsma MB, Remuzzi G, Renzaho AMN, Resnikoff S, Reynales-Shigematsu LM, Rezaei S, Ribeiro ALP, Rivera JA, Roba KT, Rodríguez-Ramírez S, Roever L, Román Y, Ronfani L, Roshandel G, Rostami A, Roth GA, Rothenbacher D, Roy A, Rubagotti E, Rushton L, Sabanayagam C, Sachdev PS, Saddik B, Sadeghi E, Saeedi Moghaddam S, Safari H, Safari Y, Safari-Faramani R, Safdarian M, Safi S, Safiri S, Sagar R, Sahebkar A, Sahraian MA, Sajadi HS, Salam N, Salamati P, Saleem Z, Salimi Y, Salimzadeh H, Salomon JA, Salvi DD, Salz I, Samy AM, Sanabria J, Sanchez-Niño MD, Sánchez-Pimienta TG, Sanders T, Sang Y, Santomauro DF, Santos IS, Santos JV, Santric Milicevic MM, Sao Jose BP, Sardana M, Sarker AR, Sarmiento-Suárez R, Sarrafzadegan N, Sartorius B, Sarvi S, Sathian B, Satpathy M, Sawant AR, Sawhney M, Saylan M, Sayyah M, Schaeffner E, Schmidt MI, Schneider IJC, Schöttker B, Schutte AE, Schwebel DC, Schwendicke F, Scott JG, Seedat S, Sekerija M, Sepanlou SG, Serre ML, Serván-Mori E, Seyedmousavi S, Shabaninejad H, Shaddick G, Shafieesabet A, Shahbazi M, Shaheen AA, Shaikh MA, Shamah Levy T, Shams-Beyranvand M, Shamsi M, Sharafi H, Sharafi K, Sharif M, Sharif-Alhoseini M, Sharifi H, Sharma J, Sharma M, Sharma R, She J, Sheikh A, Shi P, Shibuya K, Shiferaw MS, Shigematsu M, Shin MJ, Shiri R, Shirkoohi R, Shiue I, Shokraneh F, Shoman H, Shrime MG, Shupler MS, Si S, Siabani S, Sibai AM, Siddiqi TJ, Sigfusdottir ID, Sigurvinsdottir R, Silva DAS, Silva JP, Silveira DGA, Singh JA, Singh NP, Singh V, Sinha DN, Skiadaresi E, Skirbekk V, Smith DL, Smith M, Sobaih BH, Sobhani S, Somayaji R, Soofi M, Sorensen RJD, Soriano JB, Soyiri IN, Spinelli A, Sposato LA, Sreeramareddy CT, Srinivasan V, Starodubov VI, Steckling N, Stein DJ, Stein MB, Stevanovic G, Stockfelt L, Stokes MA, Sturua L, Subart ML, Sudaryanto A, Sufiyan MB, Sulo G, Sunguya BF, Sur PJ, Sykes BL, Szoeke CEI, Tabarés-Seisdedos R, Tabuchi T, Tadakamadla SK, Takahashi K, Tandon N, Tassew SG, Tavakkoli M, Taveira N, Tehrani-Banihashemi A, Tekalign TG, Tekelemedhin SW, Tekle MG, Temesgen H, Temsah MH, Temsah O, Terkawi AS, Tessema B, Teweldemedhin M, Thankappan KR, Theis A, Thirunavukkarasu S, Thomas HJ, Thomas ML, Thomas N, Thurston GD, Tilahun B, Tillmann T, To QG, Tobollik M, Tonelli M, Topor-Madry R, Torre AE, Tortajada-Girbés M, Touvier M, Tovani-Palone MR, Towbin JA, Tran BX, Tran KB, Truelsen TC, Truong NT, Tsadik AG, Tudor Car L, Tuzcu EM, Tymeson HD, Tyrovolas S, Ukwaja KN, Ullah I, Updike RL, Usman MS, Uthman OA, Vaduganathan M, Vaezi A, Valdez PR, Van Donkelaar A, Varavikova E, Varughese S, Vasankari TJ, Venkateswaran V, Venketasubramanian N, Villafaina S, Violante FS, Vladimirov SK, Vlassov V, Vollset SE, Vos T, Vosoughi K, Vu GT, Vujcic IS, Wagnew FS, Waheed Y, Waller SG, Walson JL, Wang Y, Wang Y, Wang YP, Weiderpass E, Weintraub RG, Weldegebreal F, Werdecker A, Werkneh AA, West JJ, Westerman R, Whiteford HA, Widecka J, Wijeratne T, Winkler AS, Wiyeh AB, Wiysonge CS, Wolfe CDA, Wong TY, Wu S, Xavier D, Xu G, Yadgir S, Yadollahpour A, Yahyazadeh Jabbari SH, Yamada T, Yan LL, Yano Y, Yaseri M, Yasin YJ, Yeshaneh A, Yimer EM, Yip P, Yisma E, Yonemoto N, Yoon SJ, Yotebieng M, Younis MZ, Yousefifard M, Yu C, Zaidi Z, Zaman SB, Zamani M, Zavala-Arciniega L, Zhang AL, Zhang H, Zhang K, Zhou M, Zimsen SRM, Zodpey S, Murray CJL. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018; 392:1923-1994. [PMID: 30496105 PMCID: PMC6227755 DOI: 10.1016/s0140-6736(18)32225-6] [Citation(s) in RCA: 2618] [Impact Index Per Article: 436.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2018] [Revised: 08/31/2018] [Accepted: 09/05/2018] [Indexed: 02/06/2023]
Abstract
BACKGROUND The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 comparative risk assessment (CRA) is a comprehensive approach to risk factor quantification that offers a useful tool for synthesising evidence on risks and risk-outcome associations. With each annual GBD study, we update the GBD CRA to incorporate improved methods, new risks and risk-outcome pairs, and new data on risk exposure levels and risk-outcome associations. METHODS We used the CRA framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017. This study included 476 risk-outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk and exposure estimates from 46 749 randomised controlled trials, cohort studies, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. We explored the relationship between development and risk exposure by modelling the relationship between the Socio-demographic Index (SDI) and risk-weighted exposure prevalence and estimated expected levels of exposure and risk-attributable burden by SDI. Finally, we explored temporal changes in risk-attributable DALYs by decomposing those changes into six main component drivers of change as follows: (1) population growth; (2) changes in population age structures; (3) changes in exposure to environmental and occupational risks; (4) changes in exposure to behavioural risks; (5) changes in exposure to metabolic risks; and (6) changes due to all other factors, approximated as the risk-deleted death and DALY rates, where the risk-deleted rate is the rate that would be observed had we reduced the exposure levels to the TMREL for all risk factors included in GBD 2017. FINDINGS In 2017, 34·1 million (95% uncertainty interval [UI] 33·3-35·0) deaths and 1·21 billion (1·14-1·28) DALYs were attributable to GBD risk factors. Globally, 61·0% (59·6-62·4) of deaths and 48·3% (46·3-50·2) of DALYs were attributed to the GBD 2017 risk factors. When ranked by risk-attributable DALYs, high systolic blood pressure (SBP) was the leading risk factor, accounting for 10·4 million (9·39-11·5) deaths and 218 million (198-237) DALYs, followed by smoking (7·10 million [6·83-7·37] deaths and 182 million [173-193] DALYs), high fasting plasma glucose (6·53 million [5·23-8·23] deaths and 171 million [144-201] DALYs), high body-mass index (BMI; 4·72 million [2·99-6·70] deaths and 148 million [98·6-202] DALYs), and short gestation for birthweight (1·43 million [1·36-1·51] deaths and 139 million [131-147] DALYs). In total, risk-attributable DALYs declined by 4·9% (3·3-6·5) between 2007 and 2017. In the absence of demographic changes (ie, population growth and ageing), changes in risk exposure and risk-deleted DALYs would have led to a 23·5% decline in DALYs during that period. Conversely, in the absence of changes in risk exposure and risk-deleted DALYs, demographic changes would have led to an 18·6% increase in DALYs during that period. The ratios of observed risk exposure levels to exposure levels expected based on SDI (O/E ratios) increased globally for unsafe drinking water and household air pollution between 1990 and 2017. This result suggests that development is occurring more rapidly than are changes in the underlying risk structure in a population. Conversely, nearly universal declines in O/E ratios for smoking and alcohol use indicate that, for a given SDI, exposure to these risks is declining. In 2017, the leading Level 4 risk factor for age-standardised DALY rates was high SBP in four super-regions: central Europe, eastern Europe, and central Asia; north Africa and Middle East; south Asia; and southeast Asia, east Asia, and Oceania. The leading risk factor in the high-income super-region was smoking, in Latin America and Caribbean was high BMI, and in sub-Saharan Africa was unsafe sex. O/E ratios for unsafe sex in sub-Saharan Africa were notably high, and those for alcohol use in north Africa and the Middle East were notably low. INTERPRETATION By quantifying levels and trends in exposures to risk factors and the resulting disease burden, this assessment offers insight into where past policy and programme efforts might have been successful and highlights current priorities for public health action. Decreases in behavioural, environmental, and occupational risks have largely offset the effects of population growth and ageing, in relation to trends in absolute burden. Conversely, the combination of increasing metabolic risks and population ageing will probably continue to drive the increasing trends in non-communicable diseases at the global level, which presents both a public health challenge and opportunity. We see considerable spatiotemporal heterogeneity in levels of risk exposure and risk-attributable burden. Although levels of development underlie some of this heterogeneity, O/E ratios show risks for which countries are overperforming or underperforming relative to their level of development. As such, these ratios provide a benchmarking tool to help to focus local decision making. Our findings reinforce the importance of both risk exposure monitoring and epidemiological research to assess causal connections between risks and health outcomes, and they highlight the usefulness of the GBD study in synthesising data to draw comprehensive and robust conclusions that help to inform good policy and strategic health planning. FUNDING Bill & Melinda Gates Foundation.
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James SL, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, Abbastabar H, Abd-Allah F, Abdela J, Abdelalim A, Abdollahpour I, Abdulkader RS, Abebe Z, Abera SF, Abil OZ, Abraha HN, Abu-Raddad LJ, Abu-Rmeileh NME, Accrombessi MMK, Acharya D, Acharya P, Ackerman IN, Adamu AA, Adebayo OM, Adekanmbi V, Adetokunboh OO, Adib MG, Adsuar JC, Afanvi KA, Afarideh M, Afshin A, Agarwal G, Agesa KM, Aggarwal R, Aghayan SA, Agrawal S, Ahmadi A, Ahmadi M, Ahmadieh H, Ahmed MB, Aichour AN, Aichour I, Aichour MTE, Akinyemiju T, Akseer N, Al-Aly Z, Al-Eyadhy A, Al-Mekhlafi HM, Al-Raddadi RM, Alahdab F, Alam K, Alam T, Alashi A, Alavian SM, Alene KA, Alijanzadeh M, Alizadeh-Navaei R, Aljunid SM, Alkerwi A, Alla F, Allebeck P, Alouani MML, Altirkawi K, Alvis-Guzman N, Amare AT, Aminde LN, Ammar W, Amoako YA, Anber NH, Andrei CL, Androudi S, Animut MD, Anjomshoa M, Ansha MG, Antonio CAT, Anwari P, Arabloo J, Arauz A, Aremu O, Ariani F, Armoon B, Ärnlöv J, Arora A, Artaman A, Aryal KK, Asayesh H, Asghar RJ, Ataro Z, Atre SR, Ausloos M, Avila-Burgos L, Avokpaho EFGA, Awasthi A, Ayala Quintanilla BP, Ayer R, Azzopardi PS, Babazadeh A, Badali H, Badawi A, Bali AG, Ballesteros KE, Ballew SH, Banach M, Banoub JAM, Banstola A, Barac A, Barboza MA, Barker-Collo SL, Bärnighausen TW, Barrero LH, Baune BT, Bazargan-Hejazi S, Bedi N, Beghi E, Behzadifar M, Behzadifar M, Béjot Y, Belachew AB, Belay YA, Bell ML, Bello AK, Bensenor IM, Bernabe E, Bernstein RS, Beuran M, Beyranvand T, Bhala N, Bhattarai S, Bhaumik S, Bhutta ZA, Biadgo B, Bijani A, Bikbov B, Bilano V, Bililign N, Bin Sayeed MS, Bisanzio D, Blacker BF, Blyth FM, Bou-Orm IR, Boufous S, Bourne R, Brady OJ, Brainin M, Brant LC, Brazinova A, Breitborde NJK, Brenner H, Briant PS, Briggs AM, Briko AN, Britton G, Brugha T, Buchbinder R, Busse R, Butt ZA, Cahuana-Hurtado L, Cano J, Cárdenas R, Carrero JJ, Carter A, Carvalho F, Castañeda-Orjuela CA, Castillo Rivas J, Castro F, Catalá-López F, Cercy KM, Cerin E, Chaiah Y, Chang AR, Chang HY, Chang JC, Charlson FJ, Chattopadhyay A, Chattu VK, Chaturvedi P, Chiang PPC, Chin KL, Chitheer A, Choi JYJ, Chowdhury R, Christensen H, Christopher DJ, Cicuttini FM, Ciobanu LG, Cirillo M, Claro RM, Collado-Mateo D, Cooper C, Coresh J, Cortesi PA, Cortinovis M, Costa M, Cousin E, Criqui MH, Cromwell EA, Cross M, Crump JA, Dadi AF, Dandona L, Dandona R, Dargan PI, Daryani A, Das Gupta R, Das Neves J, Dasa TT, Davey G, Davis AC, Davitoiu DV, De Courten B, De La Hoz FP, De Leo D, De Neve JW, Degefa MG, Degenhardt L, Deiparine S, Dellavalle RP, Demoz GT, Deribe K, Dervenis N, Des Jarlais DC, Dessie GA, Dey S, Dharmaratne SD, Dinberu MT, Dirac MA, Djalalinia S, Doan L, Dokova K, Doku DT, Dorsey ER, Doyle KE, Driscoll TR, Dubey M, Dubljanin E, Duken EE, Duncan BB, Duraes AR, Ebrahimi H, Ebrahimpour S, Echko MM, Edvardsson D, Effiong A, Ehrlich JR, El Bcheraoui C, El Sayed Zaki M, El-Khatib Z, Elkout H, Elyazar IRF, Enayati A, Endries AY, Er B, Erskine HE, Eshrati B, Eskandarieh S, Esteghamati A, Esteghamati S, Fakhim H, Fallah Omrani V, Faramarzi M, Fareed M, Farhadi F, Farid TA, Farinha CSES, Farioli A, Faro A, Farvid MS, Farzadfar F, Feigin VL, Fentahun N, Fereshtehnejad SM, Fernandes E, Fernandes JC, Ferrari AJ, Feyissa GT, Filip I, Fischer F, Fitzmaurice C, Foigt NA, Foreman KJ, Fox J, Frank TD, Fukumoto T, Fullman N, Fürst T, Furtado JM, Futran ND, Gall S, Ganji M, Gankpe FG, Garcia-Basteiro AL, Gardner WM, Gebre AK, Gebremedhin AT, Gebremichael TG, Gelano TF, Geleijnse JM, Genova-Maleras R, Geramo YCD, Gething PW, Gezae KE, Ghadiri K, Ghasemi Falavarjani K, Ghasemi-Kasman M, Ghimire M, Ghosh R, Ghoshal AG, Giampaoli S, Gill PS, Gill TK, Ginawi IA, Giussani G, Gnedovskaya EV, Goldberg EM, Goli S, Gómez-Dantés H, Gona PN, Gopalani SV, Gorman TM, Goulart AC, Goulart BNG, Grada A, Grams ME, Grosso G, Gugnani HC, Guo Y, Gupta PC, Gupta R, Gupta R, Gupta T, Gyawali B, Haagsma JA, Hachinski V, Hafezi-Nejad N, Haghparast Bidgoli H, Hagos TB, Hailu GB, Haj-Mirzaian A, Haj-Mirzaian A, Hamadeh RR, Hamidi S, Handal AJ, Hankey GJ, Hao Y, Harb HL, Harikrishnan S, Haro JM, Hasan M, Hassankhani H, Hassen HY, Havmoeller R, Hawley CN, Hay RJ, Hay SI, Hedayatizadeh-Omran A, Heibati B, Hendrie D, Henok A, Herteliu C, Heydarpour S, Hibstu DT, Hoang HT, Hoek HW, Hoffman HJ, Hole MK, Homaie Rad E, Hoogar P, Hosgood HD, Hosseini SM, Hosseinzadeh M, Hostiuc M, Hostiuc S, Hotez PJ, Hoy DG, Hsairi M, Htet AS, Hu G, Huang JJ, Huynh CK, Iburg KM, Ikeda CT, Ileanu B, Ilesanmi OS, Iqbal U, Irvani SSN, Irvine CMS, Islam SMS, Islami F, Jacobsen KH, Jahangiry L, Jahanmehr N, Jain SK, Jakovljevic M, Javanbakht M, Jayatilleke AU, Jeemon P, Jha RP, Jha V, Ji JS, Johnson CO, Jonas JB, Jozwiak JJ, Jungari SB, Jürisson M, Kabir Z, Kadel R, Kahsay A, Kalani R, Kanchan T, Karami M, Karami Matin B, Karch A, Karema C, Karimi N, Karimi SM, Kasaeian A, Kassa DH, Kassa GM, Kassa TD, Kassebaum NJ, Katikireddi SV, Kawakami N, Karyani AK, Keighobadi MM, Keiyoro PN, Kemmer L, Kemp GR, Kengne AP, Keren A, Khader YS, Khafaei B, Khafaie MA, Khajavi A, Khalil IA, Khan EA, Khan MS, Khan MA, Khang YH, Khazaei M, Khoja AT, Khosravi A, Khosravi MH, Kiadaliri AA, Kiirithio DN, Kim CI, Kim D, Kim P, Kim YE, Kim YJ, Kimokoti RW, Kinfu Y, Kisa A, Kissimova-Skarbek K, Kivimäki M, Knudsen AKS, Kocarnik JM, Kochhar S, Kokubo Y, Kolola T, Kopec JA, Kosen S, Kotsakis GA, Koul PA, Koyanagi A, Kravchenko MA, Krishan K, Krohn KJ, Kuate Defo B, Kucuk Bicer B, Kumar GA, Kumar M, Kyu HH, Lad DP, Lad SD, Lafranconi A, Lalloo R, Lallukka T, Lami FH, Lansingh VC, Latifi A, Lau KMM, Lazarus JV, Leasher JL, Ledesma JR, Lee PH, Leigh J, Leung J, Levi M, Lewycka S, Li S, Li Y, Liao Y, Liben ML, Lim LL, Lim SS, Liu S, Lodha R, Looker KJ, Lopez AD, Lorkowski S, Lotufo PA, Low N, Lozano R, Lucas TCD, Lucchesi LR, Lunevicius R, Lyons RA, Ma S, Macarayan ERK, Mackay MT, Madotto F, Magdy Abd El Razek H, Magdy Abd El Razek M, Maghavani DP, Mahotra NB, Mai HT, Majdan M, Majdzadeh R, Majeed A, Malekzadeh R, Malta DC, Mamun AA, Manda AL, Manguerra H, Manhertz T, Mansournia MA, Mantovani LG, Mapoma CC, Maravilla JC, Marcenes W, Marks A, Martins-Melo FR, Martopullo I, März W, Marzan MB, Mashamba-Thompson TP, Massenburg BB, Mathur MR, Matsushita K, Maulik PK, Mazidi M, McAlinden C, McGrath JJ, McKee M, Mehndiratta MM, Mehrotra R, Mehta KM, Mehta V, Mejia-Rodriguez F, Mekonen T, Melese A, Melku M, Meltzer M, Memiah PTN, Memish ZA, Mendoza W, Mengistu DT, Mengistu G, Mensah GA, Mereta ST, Meretoja A, Meretoja TJ, Mestrovic T, Mezerji NMG, Miazgowski B, Miazgowski T, Millear AI, Miller TR, Miltz B, Mini GK, Mirarefin M, Mirrakhimov EM, Misganaw AT, Mitchell PB, Mitiku H, Moazen B, Mohajer B, Mohammad KA, Mohammadifard N, Mohammadnia-Afrouzi M, Mohammed MA, Mohammed S, Mohebi F, Moitra M, Mokdad AH, Molokhia M, Monasta L, Moodley Y, Moosazadeh M, Moradi G, Moradi-Lakeh M, Moradinazar M, Moraga P, Morawska L, Moreno Velásquez I, Morgado-Da-Costa J, Morrison SD, Moschos MM, Mountjoy-Venning WC, Mousavi SM, Mruts KB, Muche AA, Muchie KF, Mueller UO, Muhammed OS, Mukhopadhyay S, Muller K, Mumford JE, Murhekar M, Musa J, Musa KI, Mustafa G, Nabhan AF, Nagata C, Naghavi M, Naheed A, Nahvijou A, Naik G, Naik N, Najafi F, Naldi L, Nam HS, Nangia V, Nansseu JR, Nascimento BR, Natarajan G, Neamati N, Negoi I, Negoi RI, Neupane S, Newton CRJ, Ngunjiri JW, Nguyen AQ, Nguyen HT, Nguyen HLT, Nguyen HT, Nguyen LH, Nguyen M, Nguyen NB, Nguyen SH, Nichols E, Ningrum DNA, Nixon MR, Nolutshungu N, Nomura S, Norheim OF, Noroozi M, Norrving B, Noubiap JJ, Nouri HR, Nourollahpour Shiadeh M, Nowroozi MR, Nsoesie EO, Nyasulu PS, Odell CM, Ofori-Asenso R, Ogbo FA, Oh IH, Oladimeji O, Olagunju AT, Olagunju TO, Olivares PR, Olsen HE, Olusanya BO, Ong KL, Ong SK, Oren E, Ortiz A, Ota E, Otstavnov SS, Øverland S, Owolabi MO, P A M, Pacella R, Pakpour AH, Pana A, Panda-Jonas S, Parisi A, Park EK, Parry CDH, Patel S, Pati S, Patil ST, Patle A, Patton GC, Paturi VR, Paulson KR, Pearce N, Pereira DM, Perico N, Pesudovs K, Pham HQ, Phillips MR, Pigott DM, Pillay JD, Piradov MA, Pirsaheb M, Pishgar F, Plana-Ripoll O, Plass D, Polinder S, Popova S, Postma MJ, Pourshams A, Poustchi H, Prabhakaran D, Prakash S, Prakash V, Purcell CA, Purwar MB, Qorbani M, Quistberg DA, Radfar A, Rafay A, Rafiei A, Rahim F, Rahimi K, Rahimi-Movaghar A, Rahimi-Movaghar V, Rahman M, Rahman MHU, Rahman MA, Rahman SU, Rai RK, Rajati F, Ram U, Ranjan P, Ranta A, Rao PC, Rawaf DL, Rawaf S, Reddy KS, Reiner RC, Reinig N, Reitsma MB, Remuzzi G, Renzaho AMN, Resnikoff S, Rezaei S, Rezai MS, Ribeiro ALP, Roberts NLS, Robinson SR, Roever L, Ronfani L, Roshandel G, Rostami A, Roth GA, Roy A, Rubagotti E, Sachdev PS, Sadat N, Saddik B, Sadeghi E, Saeedi Moghaddam S, Safari H, Safari Y, Safari-Faramani R, Safdarian M, Safi S, Safiri S, Sagar R, Sahebkar A, Sahraian MA, Sajadi HS, Salam N, Salama JS, Salamati P, Saleem K, Saleem Z, Salimi Y, Salomon JA, Salvi SS, Salz I, Samy AM, Sanabria J, Sang Y, Santomauro DF, Santos IS, Santos JV, Santric Milicevic MM, Sao Jose BP, Sardana M, Sarker AR, Sarrafzadegan N, Sartorius B, Sarvi S, Sathian B, Satpathy M, Sawant AR, Sawhney M, Saxena S, Saylan M, Schaeffner E, Schmidt MI, Schneider IJC, Schöttker B, Schwebel DC, Schwendicke F, Scott JG, Sekerija M, Sepanlou SG, Serván-Mori E, Seyedmousavi S, Shabaninejad H, Shafieesabet A, Shahbazi M, Shaheen AA, Shaikh MA, Shams-Beyranvand M, Shamsi M, Shamsizadeh M, Sharafi H, Sharafi K, Sharif M, Sharif-Alhoseini M, Sharma M, Sharma R, She J, Sheikh A, Shi P, Shibuya K, Shigematsu M, Shiri R, Shirkoohi R, Shishani K, Shiue I, Shokraneh F, Shoman H, Shrime MG, Si S, Siabani S, Siddiqi TJ, Sigfusdottir ID, Sigurvinsdottir R, Silva JP, Silveira DGA, Singam NSV, Singh JA, Singh NP, Singh V, Sinha DN, Skiadaresi E, Slepak ELN, Sliwa K, Smith DL, Smith M, Soares Filho AM, Sobaih BH, Sobhani S, Sobngwi E, Soneji SS, Soofi M, Soosaraei M, Sorensen RJD, Soriano JB, Soyiri IN, Sposato LA, Sreeramareddy CT, Srinivasan V, Stanaway JD, Stein DJ, Steiner C, Steiner TJ, Stokes MA, Stovner LJ, Subart ML, Sudaryanto A, Sufiyan MB, Sunguya BF, Sur PJ, Sutradhar I, Sykes BL, Sylte DO, Tabarés-Seisdedos R, Tadakamadla SK, Tadesse BT, Tandon N, Tassew SG, Tavakkoli M, Taveira N, Taylor HR, Tehrani-Banihashemi A, Tekalign TG, Tekelemedhin SW, Tekle MG, Temesgen H, Temsah MH, Temsah O, Terkawi AS, Teweldemedhin M, Thankappan KR, Thomas N, Tilahun B, To QG, Tonelli M, Topor-Madry R, Topouzis F, Torre AE, Tortajada-Girbés M, Touvier M, Tovani-Palone MR, Towbin JA, Tran BX, Tran KB, Troeger CE, Truelsen TC, Tsilimbaris MK, Tsoi D, Tudor Car L, Tuzcu EM, Ukwaja KN, Ullah I, Undurraga EA, Unutzer J, Updike RL, Usman MS, Uthman OA, Vaduganathan M, Vaezi A, Valdez PR, Varughese S, Vasankari TJ, Venketasubramanian N, Villafaina S, Violante FS, Vladimirov SK, Vlassov V, Vollset SE, Vosoughi K, Vujcic IS, Wagnew FS, Waheed Y, Waller SG, Wang Y, Wang YP, Weiderpass E, Weintraub RG, Weiss DJ, Weldegebreal F, Weldegwergs KG, Werdecker A, West TE, Whiteford HA, Widecka J, Wijeratne T, Wilner LB, Wilson S, Winkler AS, Wiyeh AB, Wiysonge CS, Wolfe CDA, Woolf AD, Wu S, Wu YC, Wyper GMA, Xavier D, Xu G, Yadgir S, Yadollahpour A, Yahyazadeh Jabbari SH, Yamada T, Yan LL, Yano Y, Yaseri M, Yasin YJ, Yeshaneh A, Yimer EM, Yip P, Yisma E, Yonemoto N, Yoon SJ, Yotebieng M, Younis MZ, Yousefifard M, Yu C, Zadnik V, Zaidi Z, Zaman SB, Zamani M, Zare Z, Zeleke AJ, Zenebe ZM, Zhang K, Zhao Z, Zhou M, Zodpey S, Zucker I, Vos T, Murray CJL. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018; 392:1789-1858. [PMID: 30496104 PMCID: PMC6227754 DOI: 10.1016/s0140-6736(18)32279-7#] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Revised: 08/30/2018] [Accepted: 09/12/2018] [Indexed: 08/12/2023]
Abstract
BACKGROUND The Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017) includes a comprehensive assessment of incidence, prevalence, and years lived with disability (YLDs) for 354 causes in 195 countries and territories from 1990 to 2017. Previous GBD studies have shown how the decline of mortality rates from 1990 to 2016 has led to an increase in life expectancy, an ageing global population, and an expansion of the non-fatal burden of disease and injury. These studies have also shown how a substantial portion of the world's population experiences non-fatal health loss with considerable heterogeneity among different causes, locations, ages, and sexes. Ongoing objectives of the GBD study include increasing the level of estimation detail, improving analytical strategies, and increasing the amount of high-quality data. METHODS We estimated incidence and prevalence for 354 diseases and injuries and 3484 sequelae. We used an updated and extensive body of literature studies, survey data, surveillance data, inpatient admission records, outpatient visit records, and health insurance claims, and additionally used results from cause of death models to inform estimates using a total of 68 781 data sources. Newly available clinical data from India, Iran, Japan, Jordan, Nepal, China, Brazil, Norway, and Italy were incorporated, as well as updated claims data from the USA and new claims data from Taiwan (province of China) and Singapore. We used DisMod-MR 2.1, a Bayesian meta-regression tool, as the main method of estimation, ensuring consistency between rates of incidence, prevalence, remission, and cause of death for each condition. YLDs were estimated as the product of a prevalence estimate and a disability weight for health states of each mutually exclusive sequela, adjusted for comorbidity. We updated the Socio-demographic Index (SDI), a summary development indicator of income per capita, years of schooling, and total fertility rate. Additionally, we calculated differences between male and female YLDs to identify divergent trends across sexes. GBD 2017 complies with the Guidelines for Accurate and Transparent Health Estimates Reporting. FINDINGS Globally, for females, the causes with the greatest age-standardised prevalence were oral disorders, headache disorders, and haemoglobinopathies and haemolytic anaemias in both 1990 and 2017. For males, the causes with the greatest age-standardised prevalence were oral disorders, headache disorders, and tuberculosis including latent tuberculosis infection in both 1990 and 2017. In terms of YLDs, low back pain, headache disorders, and dietary iron deficiency were the leading Level 3 causes of YLD counts in 1990, whereas low back pain, headache disorders, and depressive disorders were the leading causes in 2017 for both sexes combined. All-cause age-standardised YLD rates decreased by 3·9% (95% uncertainty interval [UI] 3·1-4·6) from 1990 to 2017; however, the all-age YLD rate increased by 7·2% (6·0-8·4) while the total sum of global YLDs increased from 562 million (421-723) to 853 million (642-1100). The increases for males and females were similar, with increases in all-age YLD rates of 7·9% (6·6-9·2) for males and 6·5% (5·4-7·7) for females. We found significant differences between males and females in terms of age-standardised prevalence estimates for multiple causes. The causes with the greatest relative differences between sexes in 2017 included substance use disorders (3018 cases [95% UI 2782-3252] per 100 000 in males vs s1400 [1279-1524] per 100 000 in females), transport injuries (3322 [3082-3583] vs 2336 [2154-2535]), and self-harm and interpersonal violence (3265 [2943-3630] vs 5643 [5057-6302]). INTERPRETATION Global all-cause age-standardised YLD rates have improved only slightly over a period spanning nearly three decades. However, the magnitude of the non-fatal disease burden has expanded globally, with increasing numbers of people who have a wide spectrum of conditions. A subset of conditions has remained globally pervasive since 1990, whereas other conditions have displayed more dynamic trends, with different ages, sexes, and geographies across the globe experiencing varying burdens and trends of health loss. This study emphasises how global improvements in premature mortality for select conditions have led to older populations with complex and potentially expensive diseases, yet also highlights global achievements in certain domains of disease and injury. FUNDING Bill & Melinda Gates Foundation.
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Troeger C, Blacker B, Khalil IA, Rao PC, Cao J, Zimsen SRM, Albertson SB, Deshpande A, Farag T, Abebe Z, Adetifa IMO, Adhikari TB, Akibu M, Al Lami FH, Al-Eyadhy A, Alvis-Guzman N, Amare AT, Amoako YA, Antonio CAT, Aremu O, Asfaw ET, Asgedom SW, Atey TM, Attia EF, Avokpaho EFGA, Ayele HT, Ayuk TB, Balakrishnan K, Barac A, Bassat Q, Behzadifar M, Behzadifar M, Bhaumik S, Bhutta ZA, Bijani A, Brauer M, Brown A, Camargos PAM, Castañeda-Orjuela CA, Colombara D, Conti S, Dadi AF, Dandona L, Dandona R, Do HP, Dubljanin E, Edessa D, Elkout H, Endries AY, Fijabi DO, Foreman KJ, Forouzanfar MH, Fullman N, Garcia-Basteiro AL, Gessner BD, Gething PW, Gupta R, Gupta T, Hailu GB, Hassen HY, Hedayati MT, Heidari M, Hibstu DT, Horita N, Ilesanmi OS, Jakovljevic MB, Jamal AA, Kahsay A, Kasaeian A, Kassa DH, Khader YS, Khan EA, Khan MN, Khang YH, Kim YJ, Kissoon N, Knibbs LD, Kochhar S, Koul PA, Kumar GA, Lodha R, Magdy Abd El Razek H, Malta DC, Mathew JL, Mengistu DT, Mezgebe HB, Mohammad KA, Mohammed MA, Momeniha F, Murthy S, Nguyen CT, Nielsen KR, Ningrum DNA, Nirayo YL, Oren E, Ortiz JR, PA M, Postma MJ, Qorbani M, Quansah R, Rai RK, Rana SM, Ranabhat CL, Ray SE, Rezai MS, Ruhago GM, Safiri S, Salomon JA, Sartorius B, Savic M, Sawhney M, She J, Sheikh A, Shiferaw MS, Shigematsu M, Singh JA, Somayaji R, Stanaway JD, Sufiyan MB, Taffere GR, Temsah MH, Thompson MJ, Tobe-Gai R, Topor-Madry R, Tran BX, Tran TT, Tuem KB, Ukwaja KN, Vollset SE, Walson JL, Weldegebreal F, Werdecker A, West TE, Yonemoto N, Zaki MES, Zhou L, Zodpey S, Vos T, Naghavi M, Lim SS, Mokdad AH, Murray CJL, Hay SI, Reiner RC. Estimates of the global, regional, and national morbidity, mortality, and aetiologies of lower respiratory infections in 195 countries, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Infect Dis 2018; 18:1191-1210. [PMID: 30243584 PMCID: PMC6202443 DOI: 10.1016/s1473-3099(18)30310-4] [Citation(s) in RCA: 910] [Impact Index Per Article: 151.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Revised: 05/02/2018] [Accepted: 05/10/2018] [Indexed: 12/18/2022]
Abstract
BACKGROUND Lower respiratory infections are a leading cause of morbidity and mortality around the world. The Global Burden of Diseases, Injuries, and Risk Factors (GBD) Study 2016, provides an up-to-date analysis of the burden of lower respiratory infections in 195 countries. This study assesses cases, deaths, and aetiologies spanning the past 26 years and shows how the burden of lower respiratory infection has changed in people of all ages. METHODS We used three separate modelling strategies for lower respiratory infections in GBD 2016: a Bayesian hierarchical ensemble modelling platform (Cause of Death Ensemble model), which uses vital registration, verbal autopsy data, and surveillance system data to predict mortality due to lower respiratory infections; a compartmental meta-regression tool (DisMod-MR), which uses scientific literature, population representative surveys, and health-care data to predict incidence, prevalence, and mortality; and modelling of counterfactual estimates of the population attributable fraction of lower respiratory infection episodes due to Streptococcus pneumoniae, Haemophilus influenzae type b, influenza, and respiratory syncytial virus. We calculated each modelled estimate for each age, sex, year, and location. We modelled the exposure level in a population for a given risk factor using DisMod-MR and a spatio-temporal Gaussian process regression, and assessed the effectiveness of targeted interventions for each risk factor in children younger than 5 years. We also did a decomposition analysis of the change in LRI deaths from 2000-16 using the risk factors associated with LRI in GBD 2016. FINDINGS In 2016, lower respiratory infections caused 652 572 deaths (95% uncertainty interval [UI] 586 475-720 612) in children younger than 5 years (under-5s), 1 080 958 deaths (943 749-1 170 638) in adults older than 70 years, and 2 377 697 deaths (2 145 584-2 512 809) in people of all ages, worldwide. Streptococcus pneumoniae was the leading cause of lower respiratory infection morbidity and mortality globally, contributing to more deaths than all other aetiologies combined in 2016 (1 189 937 deaths, 95% UI 690 445-1 770 660). Childhood wasting remains the leading risk factor for lower respiratory infection mortality among children younger than 5 years, responsible for 61·4% of lower respiratory infection deaths in 2016 (95% UI 45·7-69·6). Interventions to improve wasting, household air pollution, ambient particulate matter pollution, and expanded antibiotic use could avert one under-5 death due to lower respiratory infection for every 4000 children treated in the countries with the highest lower respiratory infection burden. INTERPRETATION Our findings show substantial progress in the reduction of lower respiratory infection burden, but this progress has not been equal across locations, has been driven by decreases in several primary risk factors, and might require more effort among elderly adults. By highlighting regions and populations with the highest burden, and the risk factors that could have the greatest effect, funders, policy makers, and programme implementers can more effectively reduce lower respiratory infections among the world's most susceptible populations. FUNDING Bill & Melinda Gates Foundation.
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Pfeffer DA, Lucas TCD, May D, Harris J, Rozier J, Twohig KA, Dalrymple U, Guerra CA, Moyes CL, Thorn M, Nguyen M, Bhatt S, Cameron E, Weiss DJ, Howes RE, Battle KE, Gibson HS, Gething PW. malariaAtlas: an R interface to global malariometric data hosted by the Malaria Atlas Project. Malar J 2018; 17:352. [PMID: 30290815 PMCID: PMC6173876 DOI: 10.1186/s12936-018-2500-5] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Accepted: 09/29/2018] [Indexed: 12/20/2022] Open
Abstract
Background The Malaria Atlas Project (MAP) has worked to assemble and maintain a global open-access database of spatial malariometric data for over a decade. This data spans various formats and topics, including: geo-located surveys of malaria parasite rate; global administrative boundary shapefiles; and global and regional rasters representing the distribution of malaria and associated illnesses, blood disorders, and intervention coverage. MAP has recently released malariaAtlas, an R package providing a direct interface to MAP’s routinely-updated malariometric databases and research outputs. Methods and results The current paper reviews the functionality available in malariaAtlas and highlights its utility for spatial epidemiological analysis of malaria. malariaAtlas enables users to freely download, visualise and analyse global malariometric data within R. Currently available data types include: malaria parasite rate and vector occurrence point data; subnational administrative boundary shapefiles; and a large suite of rasters covering a diverse range of metrics related to malaria research. malariaAtlas is here used in two mock analyses to illustrate how this data may be incorporated into a standard R workflow for spatial analysis. Conclusions malariaAtlas is the first open-access R-interface to malariometric data, providing a new and reproducible means of accessing such data within a freely available and commonly used statistical software environment. In this way, the malariaAtlas package aims to contribute to the environment of data-sharing within the malaria research community. Electronic supplementary material The online version of this article (10.1186/s12936-018-2500-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Daniel A Pfeffer
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FY, UK
| | - Timothy C D Lucas
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FY, UK.
| | - Daniel May
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FY, UK
| | - Joseph Harris
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FY, UK
| | - Jennifer Rozier
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FY, UK
| | - Katherine A Twohig
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FY, UK
| | - Ursula Dalrymple
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FY, UK
| | - Carlos A Guerra
- Sanaria Institute for Global Health & Tropical Medicine, Rockville, MD, 20850, USA
| | - Catherine L Moyes
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FY, UK
| | - Mike Thorn
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FY, UK
| | - Michele Nguyen
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FY, UK
| | - Samir Bhatt
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FY, UK.,Department of Infectious Disease Epidemiology, Imperial College London, London, W2 1PG, UK
| | - Ewan Cameron
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FY, UK
| | - Daniel J Weiss
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FY, UK
| | - Rosalind E Howes
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FY, UK
| | - Katherine E Battle
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FY, UK
| | - Harry S Gibson
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FY, UK
| | - Peter W Gething
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FY, UK
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Reiner RC, Graetz N, Casey DC, Troeger C, Garcia GM, Mosser JF, Deshpande A, Swartz SJ, Ray SE, Blacker BF, Rao PC, Osgood-Zimmerman A, Burstein R, Pigott DM, Davis IM, Letourneau ID, Earl L, Ross JM, Khalil IA, Farag TH, Brady OJ, Kraemer MUG, Smith DL, Bhatt S, Weiss DJ, Gething PW, Kassebaum NJ, Mokdad AH, Murray CJL, Hay SI. Variation in Childhood Diarrheal Morbidity and Mortality in Africa, 2000-2015. N Engl J Med 2018; 379:1128-1138. [PMID: 30231224 PMCID: PMC6078160 DOI: 10.1056/nejmoa1716766] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND Diarrheal diseases are the third leading cause of disease and death in children younger than 5 years of age in Africa and were responsible for an estimated 30 million cases of severe diarrhea (95% credible interval, 27 million to 33 million) and 330,000 deaths (95% credible interval, 270,000 to 380,000) in 2015. The development of targeted approaches to address this burden has been hampered by a paucity of comprehensive, fine-scale estimates of diarrhea-related disease and death among and within countries. METHODS We produced annual estimates of the prevalence and incidence of diarrhea and diarrhea-related mortality with high geographic detail (5 km2) across Africa from 2000 through 2015. Estimates were created with the use of Bayesian geostatistical techniques and were calibrated to the results from the Global Burden of Diseases, Injuries, and Risk Factors Study 2016. RESULTS The results revealed geographic inequality with regard to diarrhea risk in Africa. Of the estimated 330,000 childhood deaths that were attributable to diarrhea in 2015, more than 50% occurred in 55 of the 782 first-level administrative subdivisions (e.g., states). In 2015, mortality rates among first-level administrative subdivisions in Nigeria differed by up to a factor of 6. The case fatality rates were highly varied at the national level across Africa, with the highest values observed in Benin, Lesotho, Mali, Nigeria, and Sierra Leone. CONCLUSIONS Our findings showed concentrated areas of diarrheal disease and diarrhea-related death in countries that had a consistently high burden as well as in countries that had considerable national-level reductions in diarrhea burden. (Funded by the Bill and Melinda Gates Foundation.).
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Affiliation(s)
- Robert C Reiner
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Nicholas Graetz
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Daniel C Casey
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Christopher Troeger
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Gregory M Garcia
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Jonathan F Mosser
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Aniruddha Deshpande
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Scott J Swartz
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Sarah E Ray
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Brigette F Blacker
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Puja C Rao
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Aaron Osgood-Zimmerman
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Roy Burstein
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - David M Pigott
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Ian M Davis
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Ian D Letourneau
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Lucas Earl
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Jennifer M Ross
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Ibrahim A Khalil
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Tamer H Farag
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Oliver J Brady
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Moritz U G Kraemer
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - David L Smith
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Samir Bhatt
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Daniel J Weiss
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Peter W Gething
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Nicholas J Kassebaum
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Ali H Mokdad
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Christopher J L Murray
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Simon I Hay
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
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48
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Graetz N, Friedman J, Osgood-Zimmerman A, Burstein R, Biehl MH, Shields C, Mosser JF, Casey DC, Deshpande A, Earl L, Reiner RC, Ray SE, Fullman N, Levine AJ, Stubbs RW, Mayala BK, Longbottom J, Browne AJ, Bhatt S, Weiss DJ, Gething PW, Mokdad AH, Lim SS, Murray CJL, Gakidou E, Hay SI. Mapping local variation in educational attainment across Africa. Nature 2018; 555:48-53. [PMID: 29493588 PMCID: PMC6346272 DOI: 10.1038/nature25761] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Accepted: 01/17/2018] [Indexed: 11/09/2022]
Abstract
Educational attainment for women of reproductive age is linked to reduced child and maternal mortality, lower fertility and improved reproductive health. Comparable analyses of attainment exist only at the national level, potentially obscuring patterns in subnational inequality. Evidence suggests that wide disparities between urban and rural populations exist, raising questions about where the majority of progress towards the education targets of the Sustainable Development Goals is occurring in African countries. Here we explore within-country inequalities by predicting years of schooling across five by five kilometre grids, generating estimates of average educational attainment by age and sex at subnational levels. Despite marked progress in attainment from 2000 to 2015 across Africa, substantial differences persist between locations and sexes. These differences have widened in many countries, particularly across the Sahel. These high-resolution, comparable estimates improve the ability of decision-makers to plan the precisely targeted interventions that will be necessary to deliver progress during the era of the Sustainable Development Goals.
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Affiliation(s)
- Nicholas Graetz
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Joseph Friedman
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Aaron Osgood-Zimmerman
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Roy Burstein
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Molly H Biehl
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Chloe Shields
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Jonathan F Mosser
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Daniel C Casey
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Aniruddha Deshpande
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Lucas Earl
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Robert C Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Sarah E Ray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Nancy Fullman
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Aubrey J Levine
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Rebecca W Stubbs
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Benjamin K Mayala
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Joshua Longbottom
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
| | - Annie J Browne
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
| | - Samir Bhatt
- Department of Infectious Disease Epidemiology, Imperial College London, London SW7 2AZ, UK
| | - Daniel J Weiss
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
| | - Peter W Gething
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
| | - Ali H Mokdad
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Stephen S Lim
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Christopher J L Murray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Emmanuela Gakidou
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Simon I Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA.,Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
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49
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Osgood-Zimmerman A, Millear AI, Stubbs RW, Shields C, Pickering BV, Earl L, Graetz N, Kinyoki DK, Ray SE, Bhatt S, Browne AJ, Burstein R, Cameron E, Casey DC, Deshpande A, Fullman N, Gething PW, Gibson HS, Henry NJ, Herrero M, Krause LK, Letourneau ID, Levine AJ, Liu PY, Longbottom J, Mayala BK, Mosser JF, Noor AM, Pigott DM, Piwoz EG, Rao P, Rawat R, Reiner RC, Smith DL, Weiss DJ, Wiens KE, Mokdad AH, Lim SS, Murray CJL, Kassebaum NJ, Hay SI. Mapping child growth failure in Africa between 2000 and 2015. Nature 2018; 555:41-47. [PMID: 29493591 PMCID: PMC6346257 DOI: 10.1038/nature25760] [Citation(s) in RCA: 121] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Accepted: 01/17/2018] [Indexed: 12/28/2022]
Abstract
Insufficient growth during childhood is associated with poor health outcomes and an increased risk of death. Between 2000 and 2015, nearly all African countries demonstrated improvements for children under 5 years old for stunting, wasting, and underweight, the core components of child growth failure. Here we show that striking subnational heterogeneity in levels and trends of child growth remains. If current rates of progress are sustained, many areas of Africa will meet the World Health Organization Global Targets 2025 to improve maternal, infant and young child nutrition, but high levels of growth failure will persist across the Sahel. At these rates, much, if not all of the continent will fail to meet the Sustainable Development Goal target—to end malnutrition by 2030. Geospatial estimates of child growth failure provide a baseline for measuring progress as well as a precision public health platform to target interventions to those populations with the greatest need, in order to reduce health disparities and accelerate progress. Geospatial estimates of child growth failure in Africa provide a baseline for measuring progress and a precision public health platform to target interventions to those populations with the greatest need.
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Affiliation(s)
- Aaron Osgood-Zimmerman
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Anoushka I Millear
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Rebecca W Stubbs
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Chloe Shields
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Brandon V Pickering
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Lucas Earl
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Nicholas Graetz
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Damaris K Kinyoki
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Sarah E Ray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Samir Bhatt
- Department of Infectious Disease Epidemiology, Imperial College London, London SW7 2AZ, UK
| | - Annie J Browne
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
| | - Roy Burstein
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Ewan Cameron
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
| | - Daniel C Casey
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Aniruddha Deshpande
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Nancy Fullman
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Peter W Gething
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
| | - Harry S Gibson
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
| | - Nathaniel J Henry
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Mario Herrero
- Commonwealth Scientific and Industrial Research Organisation, St Lucia, Queensland 4067, Australia
| | | | - Ian D Letourneau
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Aubrey J Levine
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Patrick Y Liu
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Joshua Longbottom
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
| | - Benjamin K Mayala
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Jonathan F Mosser
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Abdisalan M Noor
- Kenya Medical Research Institute-Wellcome Trust Collaborative Programme, Nairobi, Kenya.,Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford OX3 7FZ, USA
| | - David M Pigott
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Ellen G Piwoz
- Bill & Melinda Gates Foundation, Seattle, Washington 98109, USA
| | - Puja Rao
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Rahul Rawat
- Bill & Melinda Gates Foundation, Seattle, Washington 98109, USA
| | - Robert C Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - David L Smith
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Daniel J Weiss
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
| | - Kirsten E Wiens
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Ali H Mokdad
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Stephen S Lim
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Christopher J L Murray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Nicholas J Kassebaum
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA.,Department of Anesthesiology and Pain Medicine, Seattle Children's Hospital, Seattle, Washington 98105, USA
| | - Simon I Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA.,Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
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Dalrymple U, Arambepola R, Gething PW, Cameron E. How long do rapid diagnostic tests remain positive after anti-malarial treatment? Malar J 2018; 17:228. [PMID: 29884184 PMCID: PMC5994115 DOI: 10.1186/s12936-018-2371-9] [Citation(s) in RCA: 85] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Accepted: 05/28/2018] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Rapid diagnostic tests (RDTs) are increasingly becoming a paradigm for both clinical diagnosis of malaria infections and for estimating community parasite prevalence in household malaria indicator surveys in malaria-endemic countries. The antigens detected by RDTs are known to persist in the blood after treatment with anti-malarials, but reports on the duration of persistence (and the effect this has on RDT positivity) of these antigens post-treatment have been variable. METHODS In this review, published studies on the persistence of positivity of RDTs post-treatment are collated, and a bespoke Bayesian survival model is fit to estimate the number of days RDTs remain positive after treatment. RESULTS Half of RDTs that detect the antigen histidine-rich protein II (HRP2) are still positive 15 (5-32) days post-treatment, 13 days longer than RDTs that detect the antigen Plasmodium lactate dehydrogenase, and that 5% of HRP2 RDTs are still positive 36 (21-61) days after treatment. The duration of persistent positivity for combination RDTs that detect both antigens falls between that for HRP2- or pLDH-only RDTs, with half of RDTs remaining positive at 7 (2-20) days post-treatment. This study shows that children display persistent RDT positivity for longer after treatment than adults, and that persistent positivity is more common when an individual is treated with artemisinin combination therapy than when treated with other anti-malarials. CONCLUSIONS RDTs remain positive for a highly variable amount of time after treatment with anti-malarials, and the duration of positivity is highly dependent on the type of RDT used for diagnosis. Additionally, age and treatment both impact the duration of persistence of RDT positivity. The results presented here suggest that caution should be taken when using RDT-derived diagnostic outcomes from cross-sectional data where individuals have had a recent history of anti-malarial treatment.
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Affiliation(s)
- Ursula Dalrymple
- Department of Zoology, University of Oxford, New Radcliffe House, Radcliffe Observatory Quarter, Woodstock Rd, Oxford, OX2 6GG, UK. .,Big Data Institute, University of Oxford, Li Ka Shing Centre for Health Information and Discovery, Old Road Campus, Oxford, OX3 7LF, UK.
| | - Rohan Arambepola
- Big Data Institute, University of Oxford, Li Ka Shing Centre for Health Information and Discovery, Old Road Campus, Oxford, OX3 7LF, UK
| | - Peter W Gething
- Big Data Institute, University of Oxford, Li Ka Shing Centre for Health Information and Discovery, Old Road Campus, Oxford, OX3 7LF, UK
| | - Ewan Cameron
- Big Data Institute, University of Oxford, Li Ka Shing Centre for Health Information and Discovery, Old Road Campus, Oxford, OX3 7LF, UK
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